Automated Prognosis of Alzheimer's Disease using Machine Learning Classifiers on Spontaneous Speech features

Early prediction of Alzheimer's disease and related Dementia has been a great challenge. Recently, preliminary research has shown that neurological symptoms in Covid-19 patients may accelerate the onset of Alzheimer's disease. With such a further rise in Alzheimer's and related Dementia cases, having an early prediction system becomes vital. Speech can provide a non-invasive diagnostic marker for such neurodegenerative diseases. This work mainly focuses on studying significant temporal speech features extracted directly from the recordings of the Dementia bank dataset and applying Machine Learning algorithms to classify the Alzheimer's disease related Dementia Group and the healthy control group. The result shows that Support Vector Machine outperformed other machine learning algorithms with an accuracy of 87%. Compared to prior research, which used manual transcriptions provided with the dataset, this study used audio recordings from the Dementia bank dataset and an advanced Automatic Speech Recognizer to extract speech features from the audio recordings. Furthermore, this method can be applied to the spoken responses of subjects during a neuropsychological assessment. © 2023, Ismail Saritas. All rights reserved.

Use of Deep Learning and Blockchain Technologies in Healthcare Industry

This editorial summarises the special issue entitled "Deep Learning Blockchain-enabled Technology for Improved Healthcare Industrial Systems”, which deals with the intersection and use of deep learning and blockchain technologies in the healthcare industry. This special issue consists of eleven scientific articles. © 2023 by the author(s). Licensee Prague University of Economics and Business, Czech Republic.

Automatic detection of novel corona virus (SARS-CoV-2) infection in computed tomography scan based on local adaptive thresholding and kernel-support vectors

The recent studies have indicated the requisite of computed tomography scan analysis by radiologists extensively to find out the suspected patients of SARS-CoV-2 (COVID-19). The existing deep learning methods distribute one or more of the subsequent bottlenecks. Therefore, a straight forward method for detecting COVID-19 infection using real-world computed tomography scans is presented. The detection process consists of image processing techniques such as segmentation of lung parenchyma and extraction of effective texture features. The kernel-based support vector machine is employed over feature vectors for classification. The performance parameters of the proposed method are calculated and compared with the existing methodology on the same dataset. The classification results are found outperforming and the method is less probabilistic which can be further exploited for developing more realistic detection system.Copyright © 2023 Inderscience Enterprises Ltd.

Class prediction of the prevalent transmission mode of COVID-19 within a geographic area

This research developed a multinomial classification model that predicts the prevalent mode of transmission of the coronavirus from person to person within a geographic area, using data from the World Health Organization (WHO). The WHO defines four transmission modes of the coronavirus disease 2019 (COVID-19);namely, community transmission, pending (unknown), sporadic cases, and clusters of cases. The logistic regression was deployed on the COVID-19 dataset to construct a multinomial model that can predict the prevalent transmission mode of coronavirus within a geographic area. The k-fold cross validation was employed to test predictive accuracy of the model, which yielded 73% accuracy. This model can be adopted by local authorities such as regional, state, local government, and cities, to predict the prevalent transmission mode of the virus within their territories. The outcome of the prediction will determine the appropriate strategies to put in place or re-enforced to curtail further transmission.Copyright © 2023 Inderscience Enterprises Ltd.

Automated Covid-19 Disease Identification on Chest-Ct Images Using Cnn and Vgg16

The human respiratory system is most affected by COVID-19, a coronavirus illness that has been identified. Infectious disease COVID-19 was brought on by a virus that emerged in Wuhan, China, in December 2019. The key problem for healthcare professionals is early diagnosis. Medical organizations were confused in the early stages because there were no suitable medical tools or medications to detect COVID-19. Reverse Transcription Polymerase Chain Reaction, a novel diagnostic technique, was released. The COVID-19 virus congregates in the patient's nose or throat, thus swab samples from those areas are collected. There are various accuracy and testing time restrictions with this method. Medical professionals advise using a different method called CT (Computerized Tomography), which can rapidly identify the infected lung regions and detect COVID-19 at an earlier stage. With the help of chest CT images, computer scientists created a number of deep learning models to recognize the COVID-19 condition. In this paper, a model for automatic COVID-19 recognition on chest CT images is presented that is based on Convolutional Neural Networks (CNN) and VGG16. A public dataset of 14320 CT scans was used in the experiment, and the findings revealed classification accuracy for CNN and VGG16 of 96.34% and 96.99%, respectively.Copyright © 2022, Anka Publishers. All rights reserved.

Early-stage predictors of deterioration among 3145 nonsevere SARS-CoV-2-infected people community-isolated in Wuhan, China: A combination of machine learning algorithms and competing risk survival analyses.

OBJECTIVE: To determine which early-stage variables best predicted the deterioration of coronavirus disease 2019 (COVID-19) among community-isolated people infected with severe acute respiratory syndrome coronavirus 2 and to test the performance of prediction using only inexpensive-to-measure variables. METHODS: Medical records of 3145 people isolated in two Fangcang shelter hospitals (large-scale community isolation centers) from February to March 2020 were accessed. Two complementary methods-machine learning algorithms and competing risk survival analyses-were used to test potential predictors, including age, gender, severity upon admission, symptoms (general symptoms, respiratory symptoms, and gastrointestinal symptoms), computed tomography (CT) signs, and comorbid chronic diseases. All variables were measured upon (or shortly after) admission. The outcome was deterioration versus recovery of COVID-19. RESULTS: More than a quarter of the 3145 people did not present any symptoms, while one-third ended isolation due to deterioration. Machine learning models identified moderate severity upon admission, old age, and CT ground-glass opacity as the most important predictors of deterioration. Removing CT signs did not degrade the performance of models. Competing risk models identified age ≥ 35 years, male gender, moderate severity upon admission, cough, expectoration, CT patchy opacity, CT consolidation, comorbid diabetes, and comorbid cardiovascular or cerebrovascular diseases as significant predictors of deterioration, while a stuffy or runny nose as a predictor of recovery. CONCLUSIONS: Early-stage prediction of COVID-19 deterioration can be made with inexpensive-to-measure variables, such as demographic characteristics, severity upon admission, observable symptoms, and self-reported comorbid diseases, among asymptomatic people and mildly to moderately symptomatic patients.

Classification And Errors Analysis of COVID-19 Using Supervised Machine Learning Algorithm and Visualisation

During pandemic many people died as a result of the covid-19 sickness, which appeared in 2019 and spread over the world. The objective of research work is to wards the occurrence of COVID to improve classification accuracy and threshold curve predictions on real-life dataset for Receiver Operator Characteristics (ROC) value. This paper goals the real-life COVID patients from the five countries to test the experiment. The proposed methodology involves of two steps;used Weka for calculating the accuracy by applying Decision Table machine learning classifier and compare the results of all the five countries, secondly, the improvement in ROC value in terms of initial care predictions by area under ROC analysis. For our COVID dataset has 209 instances and 16 attributes, Weka has performed on the number of training instances are 184, number of Rules applied is 20, search direction has been applied in forward direction, total number of subsets evaluated is 96, merit of best subset found is 82.609 and time taken to build model is 0. 06 seconds. One advantage of our suggested mode list hat it keeps the original data intact, ensuring experiment quality. A further advantage is that the model can be used with additional data sets to produce the highest accuracy and ROC analysis out comes.Copyright © 2022, Anka Publishers. All rights reserved.

Smart doors - A Machine Learning Approach to monitor body temperature and detect face mask

In recent years Internet of Things(IoT) plays a vital role in automation. Nearly millions of people have been affected by the threatening disease COVID-19 (coronavirus), who are either sick or being killed due to the spread of the disease. The densely populated world possesses a threat of spreading such infectious diseases rapidly. So, there is a need for supervision of people's health status working in large organizations/institutions. This paper emphasizes the automation in monitoring the temperature of human beings and face mask detection so that spreading of infectious disease like COVID-19 can be brought down. The proposed solution aids the institutions/organizations to find out the infected person and take necessary precaution at an earlier stage to avoid spreading of the disease to the other healthy persons. This prototype overcomes the drawback of existing ideas in which affected individuals are frequently exposed to high radiation devices. The idea comes with the provision of ensuring the operation of the system only in the presence of human beings and also it paves the way to install a low cost set-up. The system makes use of sensor technology to spot the common symptoms of the disease and machine learning algorithm to ensure people are wearing masks. The obtained data gets stored on the cloud and analyzed by the organizations/institution's authorities. The lid present in the entrance is opened for the people with normal constraints. The whole scheme helps the larger organizations/institutions to avoid spreading of infectious diseases. © 2022 IEEE.

Application of Machine Learning in the Fight against the COVID-19 Pandemic: A Review

Introduction: Machine learning (ML) plays a significant role in the fight against the COVID-19 (officially known as SARS-CoV-2) pandemic. ML techniques enable the rapid detection of patterns and trends in large datasets. Therefore, ML provides efficient methods to generate knowledge from structured and unstructured data. This potential is particularly significant when the pandemic affects all aspects of human life. It is necessary to collect a large amount of data to identify methods to prevent the spread of infection, early detection, reduction of consequences, and finding appropriate medicine. Modern information and communication technologies (ICT) such as the Internet of Things (IoT) allow the collection of large amounts of data from various sources. Thus, we can create predictive ML-based models for assessments, predictions, and decisions. Method(s): This is a review article based on previous studies and scientifically proven knowledge. In this paper, bibliometric data from authoritative databases of research publications (Web of Science, Scopus, PubMed) are combined for bibliometric analyses in the context of ML applications for COVID-19. Aim(s): This paper reviews some ML-based applications used for mitigating COVID-19. We aimed to identify and review ML potentials and solutions for mitigating the COVID-19 pandemic as well as to present some of the most commonly used ML techniques, algorithms, and datasets applied in the context of COVID-19. Also, we provided some insights into specific emerging ideas and open issues to facilitate future research. Conclusion(s): ML is an effective tool for diagnosing and early detection of symptoms, predicting the spread of a pandemic, developing medicines and vaccines, etc.Copyright © 2022 Sciendo. All rights reserved.

Prediction of the COVID-19 infectivity and the sustainable impact on public health under deep learning algorithm.

The aim is to explore the development trend of COVID-19 (Corona Virus Disease 2019) and predict the infectivity of 2019-nCoV (2019 Novel Coronavirus), as well as its impact on public health. First, the existing data are analyzed through data pre-processing to extract useful feature factors. Then, the LSTM (Long-Short Term Memory) prediction model in the deep learning algorithm is used to predict the epidemic situation in Hubei Province, outside Hubei nationwide, and the whole country, respectively. Meanwhile, the impact of intervention time changes on the epidemic situation is compared. The results show that the prediction results are almost consistent with the actual values. Specifically, Hubei Province abolishes quarantine restrictions after the Spring Festival holiday, and the first COVID-19 peak is reached in late February, while the second COVID-19 peak has been reached in early March. Finally, the cumulative number of diagnoses reaches 85,000 cases, with an increase of 15,000 cases compared with the nationwide cases outside Hubei under the continuous implementation of prevention and control measures. Under the prediction of the proposed LSTM model, if the nationwide implementation of prevention and control interventions is postponed by 5 days, the epidemic will peak in early March, and the cumulative number of diagnoses will be about 200,000; and if the intervention measures are implemented five days earlier, the epidemic will peak in mid-February, with a cumulative number of diagnoses of approximately 40,000. Meanwhile, the proposed LSTM model predicts the RMSE values of the epidemic situation in Hubei Province, outside Hubei nationwide, and the whole country as 34.63, 75.42, and 50.27, respectively. Under model comparison analysis, the prediction error of the proposed LSTM model is small and has better applicability over similar algorithms. The results show that the LSTM model is effective and has high performance in infectious disease prediction, and the research results can provide scientific and effective references for subsequent related research.

Leveraging Deep Learning for Designing Healthcare Analytics Heuristic for Diagnostics.

Healthcare Informatics is a phenomenon being talked about from the early 21st century in the era in which we are living. With evolution of new computing technologies huge amount of data in healthcare is produced opening several research areas. Managing the massiveness of this data is required while extracting knowledge for decision making is the main concern of today. For this task researchers are doing explorations in big data analytics, deep learning (advanced form of machine learning known as deep neural nets), predictive analytics and various other algorithms to bring innovation in healthcare. Through all these innovations happening it is not wrong to establish that disease prediction with anticipation of its cure is no longer unrealistic. First, Dengue Fever (DF) and then Covid-19 likewise are new outbreak in infectious lethal diseases and diagnosing at all stages is crucial to decrease mortality rate. In case of Diabetes, clinicians and experts are finding challenging the timely diagnosis and analyzing the chances of developing underlying diseases. In this paper, Louvain Mani-Hierarchical Fold Learning healthcare analytics, a hybrid deep learning technique is proposed for medical diagnostics and is tested and validated using real-time dataset of 104 instances of patients with dengue fever made available by Holy Family Hospital, Pakistan and 810 instances found for infectious diseases including prognosis of; Covid-19, SARS, ARDS, Pneumocystis, Streptococcus, Chlamydophila, Klebsiella, Legionella, Lipoid, etc. on GitHub. Louvain Mani-Hierarchical Fold Learning healthcare analytics showed maximum 0.952 correlations between two clusters with Spearman when applied on 240 instances extracted from comorbidities diagnostic data model derived from 15696 endocrine records of multiple visits of 100 patients identified by a unique ID. Accuracy for induced rules is evaluated by Laplace (Fig. 8) as 0.727, 0.701 and 0.203 for 41, 18 and 24 rules, respectively. Endocrine diagnostic data is made available by Shifa International Hospital, Islamabad, Pakistan. Our results show that in future this algorithm may be tested for diagnostics on healthcare big data.

GW- CNNDC: Gradient weighted CNN model for diagnosing COVID-19 using radiography X-ray images.

COVID-19 is one of the dangerous viruses that cause death if the patient doesn't identify it in the early stages. Firstly, this virus is identified in China, Wuhan city. This virus spreads very fast compared with other viruses. Many tests are there for detecting this virus, and also side effects may find while testing this disease. Corona-virus tests are now rare; there are restricted COVID-19 testing units and they can't be made quickly enough, causing alarm. Thus, we want to depend on other determination measures. There are three distinct sorts of COVID-19 testing systems: RTPCR, CT, and CXR. There are certain limitations to RTPCR, which is the most time-consuming technique, and CT-scan results in exposure to radiation which may cause further diseases. So, to overcome these limitations, the CXR technique emits comparatively less radiation, and the patient need not be close to the medical staff. COVID-19 detection from CXR images has been tested using a diversity of pre-trained deep-learning algorithms, with the best methods being fine-tuned to maximize detection accuracy. In this work, the model called GW-CNNDC is presented. The Lung Radiography pictures are portioned utilizing the Enhanced CNN model, deployed with RESNET-50 Architecture with an image size of 255*255 pixels. Afterward, the Gradient Weighted model is applied, which shows the specific separation regardless of whether the individual is impacted by Covid-19 affected area. This framework can perform twofold class assignments with exactness and accuracy, precision, recall, F1-score, and Loss value, and the model turns out proficiently for huge datasets with less measure of time.

Feature selection for diagnose coronavirus (COVID-19) disease by neural network and Caledonian crow learning algorithm.

In this study, feature selection methods based on the new Caledonian crow learning algorithm has been introduced. In the proposed algorithms, in the first stage, the best features related to COVID-19 disease are selected by the crow learning algorithm. Coronavirus (COVIDE-19) disease using as training input to the artificial neural network. Experiments on the COVID-19 disease dataset in a Brazilian hospital show that the crow learning algorithm reduces the feature selection objective function by iteration. Decreasing the feature selection function is due to reducing the error of classifying infected people as healthy and reducing the number of features. The experimental results show that the accuracy, sensitivity, precision, and F1 of the proposed method for COVID-19 patients diagnosing are 94.31%, 94.15%, 94.38%, and 94.27%, respectively. The proposed method for identifying COVID-19 patients is more accurate than ANN, CNN, CNNLSTM, CNNRNN, LSTM, and RNN methods.

Diagnosis of Skin Cancer by Using Fuzzy-Ann Expert System with Unification of Improved Gini Index Random Forest-Based Feature

In addition to being one of the most widespread and lethal diseases in the world, skin cancer is also one of the most common types of cancer. However, due to its complexity and fuzzy nature, the clinical diagnosis process of any disease, including skin cancer, prostate cancer, coronary artery disorders, diabetes, and COVID-19, is frequently accompanied by doubt. In order to address the uncertainty and ambiguity surrounding the diagnosis of skin cancer as well as the heavier burden on the overlay of the network nodes of the fuzzy neural network system that frequently occurs due to insignificant features that are used to predict or diagnose the disease, a fuzzy neural network expert system with an improved Gini index random forest-based feature importance measure algorithm was proposed in this work. A Greater Gini Index Out of the 30 features in the dataset, the five most fitting features of the diagnostic Wisconsin breast cancer database were chosen using a random forest-based feature importance measure algorithm. Two sets of classification models were created using the logistic regression, support vector machine, k-nearest neighbour, random forest, and Gaussian naive Bayes learning algorithms. As a result, models for classification that included all features (30) and models that only used the top five features were used. The efficacy of the two sets of categorization models was assessed, and the results of the assessment were compared. The comparison's results show that the models with the fittest features outperformed those with the most complete features in terms of accuracy, sensitivity, and sensitivity. A fuzzy neural network-based expert system was therefore developed, utilising the five best features, and it achieved 99.83 percent accuracy, 99.86 percent sensitivity, and 99.64 percent specificity. The system built in this study also stands to be the best in terms of accuracy, sensitivity, and specificity when compared to prior research that used fuzzy neural networks or other applicable artificial intelligence techniques on the same dataset for the diagnosis of skin cancer. The z-test was also performed, and the test result demonstrates that the system has significantly improved accuracy for early skin cancer diagnosis. Copyright © 2023 Wolters Kluwer Medknow Publications. All rights reserved.

Smart City Construction and Management by Digital Twins and BIM Big Data in COVID-19 Scenario

With the rapid development of information technology and the spread of Corona Virus Disease 2019 (COVID-19), the government and urban managers are looking for ways to use technology to make the city smarter and safer. Intelligent transportation can play a very important role in the joint prevention. This work expects to explore the building information modeling (BIM) big data (BD) processing method of digital twins (DTs) of Smart City, thus speeding up the construction of Smart City and improve the accuracy of data processing. During construction, DTs build the same digital copy of the smart city. On this basis, BIM designs the building's keel and structure, optimizing various resources and configurations of the building. Regarding the fast data growth in smart cities, a complex data fusion and efficient learning algorithm, namely Multi-Graphics Processing Unit (GPU), is proposed to process the multi-dimensional and complex BD based on the compositive rough set model. The Bayesian network solves the multi-label classification. Each label is regarded as a Bayesian network node. Then, the structural learning approach is adopted to learn the label Bayesian network's structure from data. On the P53-old and the P53-new datasets, the running time of Multi-GPU decreases as the number of GPUs increases, approaching the ideal linear speedup ratio. With the continuous increase of K value, the deterministic information input into the tag BN will be reduced, thus reducing the classification accuracy. When K = 3, MLBN can provide the best data analysis performance. On genbase dataset, the accuracy of MLBN is 0.982 +/- 0.013. Through experiments, the BIM BD processing algorithm based on Bayesian Network Structural Learning (BNSL) helps decision-makers use complex data in smart cities efficiently.

Nowcasting Chinese GDP in a data-rich environment: Lessons from machine learning algorithms

The ability to estimate current GDP growth before official data are released, known as "nowcasting”, is crucial for the Chinese government to effectively implement economic policy and manage economic uncertainties;however, there is limited research on nowcasting China's GDP in a data-rich environment. We evaluate the performance of various machine learning algorithms, dynamic factor models, static factor models, and MIDAS regressions in nowcasting the Chinese annualised real GDP growth rate in pseudo out-of-sample exercise, using 89 macroeconomic variables from years 1995 to 2020. We find that some machine learning methods outperform the benchmark dynamic factor model. The machine learning method that deserves more attention is ridge regression, which dominates all other models not only in terms of nowcast error but also in effective recognition of the impacts of the Global Financial Crisis and Covid-19 shocks. Policy-wise, our study guides practitioners in selecting appropriate nowcasting models for China's macroeconomy.

Covid-19 Detection using PCA with EL in X-ray Images

Covid-19 is a highly contagious disease that can easily spread from infected person through mouth or nose when they breathe, sneeze, speak etc. Because of its highly contagious nature, it makes large number of people sick at a pace that can destroy any country's health system. Although most of the young and fit people have seen mild impact of Covid-19, it has proven to be severe to highly severe in people with comorbidities. Covid19 has changed the way we live and work and is making huge impact in economic, social, political environments. Diagnosis of coronavirus can be done through different tests and tools. This paper includes the role of machine learning in diagnosis of coronavirus from chest X-rays. Three commonly used classifiers were used i.e., Logistic Regression, XGBoost, and Random Forest and final model is created using all these algorithms. The main focus is achieve high accuracy. To fasten the learning process, Principal Component Analysis (PCA) is also integrated and also high discriminate features are used in order to achieve better accuracy. We have used dataset containing Chest X-Ray images for this study. Our proposed work of PCA with Ensemble Learning algorithms have shown promising signs with better results for identification of positive cases. Copyright © 2022, Anka Publishers. All rights reserved.

Estimation of Social Distance for COVID19 Prevention using K-Nearest Neighbor Algorithm through deep learning

Coronavirus disease has a crisis with high spread throughout the world during the COVID19 pandemic period. This disease can be easily spread to a group of people and increase the spread. Since it is a worldly disease and not plenty of vaccines available, social distancing is the only best approach to defend against the pandemic situation. All the affected countries' governments declared locked-down to implement social distancing. This social separation and persons not being in a mass group can slow down the spread of COVID19. It reduces the physical contact between infected persons and normal healthy persons. Almost every health organization tells that to follow social distancing people should maintain at least 6 feet of distance from each other. This research proposes a deep learning approach for social distancing which is developed for tracking and detecting people who are in indoor as well as outdoor scenarios using YOLO V3 video analytic technique. This approach focuses to inspect whether the people are maintaining social distancing in many areas, using surveillance video with measuring the distance in real-time performance. Most of the early studies of detecting social distance monitoring were based on GPS for tracking the movements of people where the signals could be lost. On the other hand, some countries use drones to detect large gatherings of people who cannot have a clear view at night times [10]. In the future, the proposed system can be used fully for detecting threats in the public crowded or it can detect any person affected by critical situations (ie fainting, Cordia arrest) or planting the crops in the forms evenly with a uniform measurement. This proposal could be used in many fields like crowd analysis, autonomous vehicles, and human action recognition and could help the government authorities to redesign the public place layout and take precautionary action in the risk zones. This system analyses the social distancing of people by calculating the distance between people to slow downing the spread of the COVID 19 virus. © 2022 IEEE.

Detection of COVID-19 Infection from Clinical Findings Using Machine Learning Algorithm

COVID-19 infection is a transmissible virus causing acute respiratory syndrome spreading worldwide. The number of patients infected by this deadly virus increases steadily, causing a high mortality rate. Hence, it is crucial to diagnose and identify the COVID-19 infection for earlier treatment of the patients. This study has applied four algorithms, namely, Logistic Regression (LR), Nu-Support Vector Machine (Nu-SVM), Multi-layer perceptron (MLP) and Naive Bayes (NB) to identify COVID-19 infection. The clinical laboratory findings of 600 individuals were taken from Hospital Isrelita Albert Einstein, Sao Paulo, Brazil, used in this study. We have selected significant features using Random forest-based recursive feature elimination for predicting the infection. Experiments are conducted with 90% training and 10% testing data. The performance result shows that the Nu-SVM algorithm obtained the prediction accuracy of 95% with 100% sensitivity and 94.23% specificity in predicting the infection. To our knowledge, the result achieved by Nu-SVM is the highest in the literature. Hence, the model can be used as a tool for the initial prediction of COVID-19 disease. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A DEEP LEARNING BASED LOCATION AWARE ENCODING NETWORK ARCHITECTURE FOR LUNG CANCER SEGMENTATION

Lung cancer (LC) is one of the most common malignant tumors, with rapid growth and early spread. LC is one of the most common malignant tumors. Lung cancer is a deadly disease, and early detection is essential. To achieve more precise diagnoses, cancer segmentation aids clinicians in determining the extent and location of cancer. But manually segmenting lung tumors from large medical images is a time-consuming and difficult task. A convolutional neural network (CNN)-based encoding network with position awareness is proposed in this study for automatically segmenting LC from computed tomography images. It is our model's design philosophy to change the usual link net architecture so that we can properly identify cancer. Our innovation resides in the manner we connect each encoder with decoder, in contrast to previous neural network topologies used for segmenting. During the encoder's many downsampling processes, spatial information is lost. By employing simply the encoder's down sampled output, it is impossible to retrieve this lost information Through the use of untrainable indices, the encoder and decoder are connected together. The output of an encoder may also be sent straight into a decoder, which can then execute segmentation on it.To conduct this study, a spatial attention-based encoder and a decoder that bypasses each encoder's input to the output of its related decoder were employed. Decoding and upsampling procedures will benefit from the spatial information that is recovered in this manner. With each layer of encoded information, the decoding process may require less parameter space, making it more efficient. Lung Image Database Consortium image collecting dataset obtained 98.5 percent accuracy in verifying the suggested system's performance. According to the study mentioned, a subjective comparison between the suggested approach and certain current methodologies is also carried out. Experiments have shown that the suggested method outperformed current technologies, allowing radiologist to more precisely locate a lung tumour while using it.