Survey of Denoising, Segmentation and Classification of Pancreatic Cancer Imaging

BACKGROUND: Pancreatic cancer is one of the most serious problems that has taken many lives worldwide. The diagnostic procedure using the traditional approaches was manual by visually analyzing the large volumes of the dataset, making it time-consuming and prone to subjective errors. Hence the need for the computer-aided diagnosis system (CADs) emerged that comprises the machine and deep learning approaches for denoising, segmentation and classification of pancreatic cancer. INTRODUCTION: There are different modalities used for the diagnosis of pancreatic cancer, such as Positron Emission Tomography/Computed Tomography (PET/CT), Magnetic Resonance Imaging (MRI), Multiparametric-MRI (Mp-MRI), Radiomics and Radio-genomics. Although these modalities gave remarkable results in diagnosis on the basis of different criteria. CT is the most commonly used modality that produces detailed and fine contrast images of internal organs of the body. However, it may also contain a certain amount of gaussian and rician noise that is necessary to be preprocessed before segmentation of the required region of interest (ROI) from the images and classification of cancer. METHOD: This paper analyzes different methodologies used for the complete diagnosis of pancreatic cancer, including the denoising, segmentation and classification, along with the challenges and future scope for the diagnosis of pancreatic cancer. RESULT: Various filters are used for denoising and image smoothening and filters as gaussian scale mixture process, non-local means, median filter, adaptive filter and average filter have been used more for better results. CONCLUSION: In terms of segmentation, atlas based region-growing method proved to give better results as compared to the state of the art whereas, for the classification, deep learning approaches outperformed other methodologies to classify the images as cancerous and non- cancerous. These methodologies have proved that CAD systems have become a better solution to the ongoing research proposals for the detection of pancreatic cancer worldwide.

An efficient ensemble based machine learning approach for predicting Chronic Kidney Disease.

BACKGROUND: Chronic kidney disease (CKD) is a long-term risk to one's health that can result in kidney failure. CKD is one of today's most serious diseases, and early detection can aid in proper treatment. Machine learning techniques have proven to be reliable in the early medical diagnosis. OBJECTIVE: The paper aims to perform CKD prediction using machine learning classification approaches. The dataset used for the present study for detecting CKD was obtained from the machine learning repository at the University of California, Irvine (UCI). METHOD: In this study, twelve machine learning-based classification algorithms with full features were used. Since the CKD dataset had a class imbalance issue, the Synthetic Minority Over-Sampling technique (SMOTE) was used to alleviate the problem of class imbalance and review the performance based on machine learning classification models using the K fold cross-validation technique. The proposed work compares the results of twelve classifiers with and without the SMOTE technique, and then the top three classifiers with the highest accuracy, Support Vector Machine, Random Forest, and Adaptive Boosting classification algorithms were selected to use the ensemble technique to improve performance. RESULTS: The accuracy achieved using a stacking classifier as an ensemble technique with cross-validation is 99.5%. CONCLUSION: The study provides an ensemble learning approach in which the top three best-performing classifiers in terms of cross-validation results are stacked in an ensemble model after balancing the dataset using SMOTE. This proposed technique could be applied to other diseases in the future, making disease detection less intrusive and cost-effective.

An empirical comparison of machine learning algorithms for the classification of brain signals to assess the impact of combined yoga and Sudarshan Kriya.

Today's fast paced life reports so much stress among people that it may lead to various psychological and physical illnesses. Yoga and meditation are the best strategies to reduce the effect of stress on physical and mental level without any side-effects. In this study, combined yoga and Sudarshan Kriya (SK) has been used as an alternative and complementary therapy for the management of stress. The aim of the study is to find a method to classify the meditator and non-meditator states with the best accuracy. The 50 subjects have been participating in this study and divided into two groups, i.e. study and control group. The subjects with regular practice of Yoga and SK are known as meditators and the ones without any practice of yoga and meditation were known as non-meditators. Electroencephalogram (EEG) signals were acquired from these both groups before and after 3 months. The statistical parameters were computed from these acquired EEG signals using Discrete Wavelet Transform (DWT). These extracted statistical parameters were given as input to the classifiers. The decision tree, discriminant analysis, logistic regression, Support Vector Machine (SVM), Weighted K- Nearest Neighbour (KNN) and ensemble classifiers were used for classification of meditator and non- meditator states from the acquired EEG signals. The results have demonstrated that the SVM method gives the highest classification accuracy as compared to other classifiers. The proposed method can be used as a diagnosis system in clinical practices.

Mechanical behaviour of 3D printed vs thermoformed clear dental aligner materials under non-linear compressive loading using FEM.

Clear dental aligners are commonly manufactured using thermoplastic materials such as Duran and Durasoft. Using conventional thermoforming methods there are inherent disadvantages including time consumption and poor geometrical accuracies that often occur. The use of digital technologies and 3D printing techniques for producing dental aligners is often preferred where possible. Innovation in 3D printing has resulted in bio-compatible materials becoming more readily available, including Formlabs Dental LT Clear resin, which is a 3D printable and Class IIa bio-compatible material. In this paper, we investigate the difference between thermoplastic materials such as Scheu-Dental Duran and Durasoft and 3D printed Dental LT using Finite Element Analysis (FEA)/Finite Element Modelling (FEM) in a dental aligner case based on an analysis of von Mises stress distribution at molars, incisors and canines for a total of 33161 nodes using Finite Element Analysis (FEA). Maximum von Mises stress distribution at all of the sections under the action of non-linear compressive forces equivalent to human biting force (up to 600 N) were discovered to vary within a range of 0.2-7.7% for Dental LT resin. The Duran and Durasoft cases were comparable, thereby widening the scope for the use of Dental LT in various dentistry applications, including clear aligners.

An overview of non-invasive imaging modalities for diagnosis of solid and cystic renal lesions.

Renal imaging is an essential investigative tool and preliminary task for determining a suitable sanative option for the treatment of kidney cancer. In recent decades, with the increasing usage of abdominal imaging, there is an upsurge in the number of adrenal incidentalomas. Among these accidentally revealed lesions, most of them are complex that warrant immediate aggressive treatment planning due to their malignant potential. The guidelines given by the American Urological Association (AUA), American College of Radiology (ACR), and European Association of Urology (EAU) vary concerning the use of ideal preliminary imaging modality to investigate the patients with suspected flank pain, hematuria, or palpable mass in the abdomen. Initially, an effort has been made to discriminate cystic and solid renal lesions which are helpful in separating benign and malignant nature as different imaging patterns are observed on distinct imaging modalities for solid and cystic renal lesions. Various attempts have been made to improve the accuracy of cancer diagnosis by employing different imaging modalities. The primary aim of this article is to study the capabilities of different imaging techniques for detecting and differentiating solid and cystic lesions to facilitate treatment planning based on computed tomography (CT), ultrasonography (US), magnetic resonance imaging (MRI), positron emission tomography (PET), and optical coherence tomography (OCT). Further, the advantages, disadvantages, new advancements, and future scope of each of the imaging modality have also been highlighted so that one can make a correct choice of imaging technique for diagnosis of a specific type of lesion. Additionally, some recommendations have also been mentioned by listing the requirements for the perfect imaging modality. Graphical abstract.

Mechanical and geometric properties of thermoformed and 3D printed clear dental aligners.

INTRODUCTION: The aim of this research was to compare compressive mechanical properties and geometric inaccuracies between conventionally manufactured thermoformed Duran clear dental aligners and 3D printed Dental Long Term (LT) resin-based clear aligners using 3D modeling and printing techniques. METHODS: Impressions of the patient's dentition were scanned and using 3D modeling software, dental models were designed and 3D printed. These printed models then underwent vacuum thermoforming to thermoform a clear Duran thermoplastic sheet of 0.75-mm thickness into clear dental aligners of the same thickness of 0.75 mm. For the same dental model, aligners were also designed and 3D printed to 0.75-mm thickness creating biocompatible clear dental aligners using Dental LT resin utilizing a Formlabs 3D printing machine for direct usage by the patients. Five observers calculated teeth height for both types of aligners for evaluation of geometric deviations. Both types of aligners were subjected to compression loading of 1000 N to evaluate their load vs displacement behavior. RESULTS: 3D printed cured clear dental aligners were found to be geometrically more accurate with an average relative difference in tooth height of 2.55% in comparison with thermoformed aligners (4.41%). Low standard deviations (0.03-0.09 mm) were observed for tooth height measurements taken by all the observers for both types of aligners. 3D printed aligners could resist a maximum load of nearly 662 N for a low displacement of 2.93 mm; whereas, thermoformed aligners could resist a load on only 105 N for 2.93-mm displacement. Thermoformed aligners deformed plastically and irreversibly for large displacements; whereas, 3D printed aligners deformed elastically with reversibility for lower displacements. CONCLUSIONS: 3D printed and suitably cured Dental LT resin-based clear dental aligners are suggested to be more suitable for patient use as they are geometrically more accurate; this presents an opportunity to make processing time savings while ensuring an aligner is mechanically stronger and elastic in comparison with the conventionally produced thermoplastic-based thermoformed clear dental aligners.

EEG signal based classification before and after combined Yoga and Sudarshan Kriya.

Nowadays, the style of living is restless and busy which has resulted in increased stress among many people. Stress causes various mental and health illness such as depression, anxiety, mood disorders, and aggressive behavior. Yoga and Sudarshan Kriya (SK) meditation are healthy ways to eradicate stress from people's lives. Based on the previous study, it has been analyzed that SK practice helps to enhance relaxation, management of emotion, alertness, focus, and antidepressant effect. In this paper, the combined impact of yoga and SK meditation has been analyzed on brain signals by using statistical parameters. To the best of the authors' knowledge, no such study has been conducted in the past. In this study, the pre and post Electroencephalogram (EEG) signals were captured from the control and study group before and after three months regular practice of combined yoga and SK. Discrete Wavelet Transform (DWT) has been used to decompose the signal into 6 sub-bands (0-4, 4-8, 8-16, 16-32, 32-64, 64-128) hertz (Hz) by using db4 wavelet for analysis, statistical features such as variance, standard deviation, kurtosis, zero crossing, maximum and minimum have been calculated from each sub-band. The obtained parameters have been validated by using Kruskal-Wallis statistical test. Further, Artificial Neural Network (ANN) has been applied on aforementioned statistical parameters to classify subjects as meditators and non-meditators. The experimental results indicated that the proposed method achieved 87.2% accuracy for classification and could be further extended to construct an accurate classification system for detection of meditators and non-meditators. This study forms a scientific foundation to encourage the use of meditation in clinical practices.