Deep learning based detection of monkeypox virus using skin lesion images.

As we set into the second half of 2022, the world is still recovering from the two-year COVID-19 pandemic. However, over the past three months, the outbreak of the Monkeypox Virus (MPV) has led to fifty-two thousand confirmed cases and over one hundred deaths. This caused the World Health Organisation to declare the outbreak a Public Health Emergency of International Concern (PHEIC). If this outbreak worsens, we could be looking at the Monkeypox virus causing the next global pandemic. As Monkeypox affects the human skin, the symptoms can be captured with regular imaging. Large samples of these images can be used as a training dataset for machine learning-based detection tools. Using a regular camera to capture the skin image of the infected person and running it against computer vision models is beneficial. In this research, we use deep learning to diagnose monkeypox from skin lesion images. Using a publicly available dataset, we tested the dataset on five pre-trained deep neural networks: GoogLeNet, Places365-GoogLeNet, SqueezeNet, AlexNet and ResNet-18. Hyperparameter was done to choose the best parameters. Performance metrics such as accuracy, precision, recall, f1-score and AUC were considered. Among the above models, ResNet18 was able to obtain the highest accuracy of 99.49%. The modified models obtained validation accuracies above 95%. The results prove that deep learning models such as the proposed model based on ResNet-18 can be deployed and can be crucial in battling the monkeypox virus. Since the used networks are optimized for efficiency, they can be used on performance limited devices such as smartphones with cameras. The addition of explainable artificial intelligence techniques LIME and GradCAM enables visual interpretation of the prediction made, helping health professionals using the model.

Machine Learning-Based Detection of Dengue from Blood Smear Images Utilizing Platelet and Lymphocyte Characteristics.

Dengue fever, also known as break-bone fever, can be life-threatening. Caused by DENV, an RNA virus from the Flaviviridae family, dengue is currently a globally important public health problem. The clinical methods available for dengue diagnosis require skilled supervision. They are manual, time-consuming, labor-intensive, and not affordable to common people. This paper describes a method that can support clinicians during dengue diagnosis. It is proposed to automate the peripheral blood smear (PBS) examination using Artificial Intelligence (AI) to aid dengue diagnosis. Nowadays, AI, especially Machine Learning (ML), is increasingly being explored for successful analyses in the biomedical field. Digital pathology coupled with AI holds great potential in developing healthcare services. The automation system developed incorporates a blob detection method to detect platelets and thrombocytopenia from the PBS images. The results achieved are clinically acceptable. Moreover, an ML-based technique is proposed to detect dengue from the images of PBS based on the lymphocyte nucleus. Ten features are extracted, including six morphological and four Gray Level Spatial Dependance Matrix (GLSDM) features, out of the lymphocyte nucleus of normal and dengue cases. Features are then subjected to various popular supervised classifiers built using a ten-fold cross-validation policy for automated dengue detection. Among all the classifiers, the best performance was achieved by Support Vector Machine (SVM) and Decision Tree (DT), each with an accuracy of 93.62%. Furthermore, 1000 deep features extracted using pre-trained MobileNetV2 and 177 textural features extracted using Local binary pattern (LBP) from the lymphocyte nucleus are subjected to feature selection. The ReliefF selected 100 most significant features are then fed to the classifiers. The best performance was attained using an SVM classifier with 95.74% accuracy. With the obtained results, it is evident that this proposed approach can efficiently contribute as an adjuvant tool for diagnosing dengue from the digital microscopic images of PBS.

Hamlet-Pattern-Based Automated COVID-19 and Influenza Detection Model Using Protein Sequences.

SARS-CoV-2 and Influenza-A can present similar symptoms. Computer-aided diagnosis can help facilitate screening for the two conditions, and may be especially relevant and useful in the current COVID-19 pandemic because seasonal Influenza-A infection can still occur. We have developed a novel text-based classification model for discriminating between the two conditions using protein sequences of varying lengths. We downloaded viral protein sequences of SARS-CoV-2 and Influenza-A with varying lengths (all 100 or greater) from the NCBI database and randomly selected 16,901 SARS-CoV-2 and 19,523 Influenza-A sequences to form a two-class study dataset. We used a new feature extraction function based on a unique pattern, HamletPat, generated from the text of Shakespeare's Hamlet, and a signum function to extract local binary pattern-like bits from overlapping fixed-length (27) blocks of the protein sequences. The bits were converted to decimal map signals from which histograms were extracted and concatenated to form a final feature vector of length 1280. The iterative Chi-square function selected the 340 most discriminative features to feed to an SVM with a Gaussian kernel for classification. The model attained 99.92% and 99.87% classification accuracy rates using hold-out (75:25 split ratio) and five-fold cross-validations, respectively. The excellent performance of the lightweight, handcrafted HamletPat-based classification model suggests that it can be a valuable tool for screening protein sequences to discriminate between SARS-CoV-2 and Influenza-A infections.

Novel Radiomics Features for Automated Detection of Cardiac Abnormality in Patients with Pacemaker.

Cardiac pacemakers are used in the treatment of patients with symptomatic bradycardia. The pacemaker paces the heart at the predetermined rate to maintain uninterrupted cardiac activity. Usually, pacemaker lead will be connected to the right atrium (RA) and right ventricle (RV) in dual-chamber pacemaker implantation and RV alone in single-chamber pacemaker implantation. This alters the route of proper conduction across the myocardial cells. The cell-to-cell conduction transmission in pacing delays the activation of selected intraventricular myocardial activation. Pacing-induced cardiomyopathy (PICM) is most commonly defined as a drop in left ventricle ejection fraction (LVEF) in the setting of chronic, high-burden right ventricle (RV) pacing. Currently, very few effective treatments are standard for PICM which rely on the detection of the RV pacing. Such treatments have primarily focused on upgrading to cardiac resynchronization therapy (CRT) when LVEF has dropped. However, the early and accurate detection of these stress factors is challenging. Cardiac desynchrony and interventricular desynchrony can be determined by various echocardiographic techniques, including M-mode, Doppler method, tissue Doppler method, and speckle tracking echocardiography which is subjective measures and shows a significant difference between RV and LV preejection period where the activation of LV is delayed considerably. Computer-aided diagnosis (CAD) is a noninvasive technique that can classify the ultrasound images of the heart in pacemaker-implanted patients and healthy patients with normal left ventricular systolic function and further detect the variations in pacemaker functions in its early stage using heart ultrasound images. Developing such a system requires a vast and diverse database to reach optimum performance. This paper proposes a novel CAD tool for the accurate detection of pacemaker variations using machine learning models of decision tree, SVM, random forest, and AdaBoost. The models have been used to extract radiomics features in terms of textures and then screened by their Relief-F scores for selection and ranking to be classified into nine groups consisting of up to 250 radiomics features. Ten best features were fed to the machine learning models. The R-wave dataset achieved a maximum test performance accuracy of 97.73% with four features in the random forest model. The T-wave dataset achieved a maximum test performance accuracy of 96.59% with three features in the SVM model. Our experimental results demonstrate the system's robustness, which can be developed as an early and accurate detection system for pacing-induced cardiomyopathy.

Diagnosis of attention deficit hyperactivity disorder using imaging and signal processing techniques.

Attention Deficit Hyperactivity Disorder (ADHD) is the most common childhood psychiatric disorder that may continue through adolescence and adulthood. Hyperactivity, inattention and impulsivity are the key behavioral features observed in children with ADHD. ADHD is normally diagnosed only when a child continues to have symptoms of hyperactivity, impulsivity and inattention at a greater degree than the normal for six months or more. In recent years there has been significant research to diagnose ADHD in a quantitative way using medical imaging and signal processing techniques. This paper presents a review of recent research on diagnosis of ADHD using medical imaging and signal processing techniques. This research is especially valuable for early diagnosis of ADHD.

Autism: cause factors, early diagnosis and therapies.

Autism spectrum disorder (ASD) is a complex neurobiological disorder characterized by neuropsychological and behavioral deficits. Cognitive impairment, lack of social skills, and stereotyped behavior are the major autistic symptoms, visible after a certain age. It is one of the fastest growing disabilities. Its current prevalence rate in the U.S. estimated by the Centers for Disease Control and Prevention is 1 in 68 births. The genetic and physiological structure of the brain is studied to determine the pathology of autism, but diagnosis of autism at an early age is challenging due to the existing phenotypic and etiological heterogeneity among ASD individuals. Volumetric and neuroimaging techniques are explored to elucidate the neuroanatomy of the ASD brain. Nuroanatomical, neurochemical, and neuroimaging biomarkers can help in the early diagnosis and treatment of ASD. This paper presents a review of the types of autism, etiologies, early detection, and treatment of ASD.

Automated diagnosis of autism: in search of a mathematical marker.

Autism is a type of neurodevelopmental disorder affecting the memory, behavior, emotion, learning ability, and communication of an individual. An early detection of the abnormality, due to irregular processing in the brain, can be achieved using electroencephalograms (EEG). The variations in the EEG signals cannot be deciphered by mere visual inspection. Computer-aided diagnostic tools can be used to recognize the subtle and invisible information present in the irregular EEG pattern and diagnose autism. This paper presents a state-of-the-art review of automated EEG-based diagnosis of autism. Various time domain, frequency domain, time-frequency domain, and nonlinear dynamics for the analysis of autistic EEG signals are described briefly. A focus of the review is the use of nonlinear dynamics and chaos theory to discover the mathematical biomarkers for the diagnosis of the autism analogous to biological markers. A combination of the time-frequency and nonlinear dynamic analysis is the most effective approach to characterize the nonstationary and chaotic physiological signals for the automated EEG-based diagnosis of autism spectrum disorder (ASD). The features extracted using these nonlinear methods can be used as mathematical markers to detect the early stage of autism and aid the clinicians in their diagnosis. This will expedite the administration of appropriate therapies to treat the disorder.