Skin cancer is caused due to unusual development of skin cells and deadly type cancer. Early diagnosis is very significant and can avoid some categories of skin cancers, such as melanoma and focal cell carcinoma. The recognition and the classification of skin malignant growth in the beginning time is expensive and challenging. The deep learning architectures such as recurrent networks and convolutional neural networks (ConvNets) are developed in the past, which are proven appropriate for non-handcrafted extraction of complex features. To additional expand the efficiency of the ConvNet models, a cascaded ensembled network that uses an integration of ConvNet and handcrafted features based multi-layer perceptron is proposed in this work. This offered model utilizes the convolutional neural network model to mine non-handcrafted image features and colour moments and texture features as handcrafted features. It is demonstrated that accuracy of ensembled deep learning model is improved to 98.3% from 85.3% of convolutional neural network model.

Alzheimer’s Disease (AD) is becoming increasingly prevalent across the globe, and various diagnostic and detection methods have been developed in recent years. Several techniques are available, including Automatic Pipeline Methods and Machine Learning Methods that utilize Biomarker Methods, Fusion, and Registration for multimodality, to pre-process medical scans. The use of automated pipelines and machine learning systems has proven beneficial in accurately identifying AD and its stages, with a success rate of over 95% for single and binary class classifications. However, there are still challenges in multi-class classification, such as distinguishing between AD and MCI, as well as sub-stages of MCI. The research also emphasizes the significance of using multi-modality approaches for effective validation in detecting AD and its stages.

Novel corona virus COVID‐19 has spread rapidly all over the world. Due to increasing COVID‐19 cases, there is a dearth of testing kits. Therefore, there is a severe need for an automatic recognition system as a solution to reduce the spreading of the COVID‐19 virus. This work offers a decision support system based on the X‐ray image to diagnose the presence of the COVID‐19 virus. A deep learning‐based computer‐aided decision support system will be capable to differentiate between COVID‐19 and pneumonia. Recently, convolutional neural network (CNN) is designed for the diagnosis of COVID‐19 patients through chest radiography (or chest X‐ray, CXR) images. However, due to the usage of CNN, there are some limitations with these decision support systems. These systems suffer with the problem of view‐invariance and loss of information due to down‐sampling. In this paper, the capsule network (CapsNet)‐based system named visual geometry group capsule network (VGG‐CapsNet) for the diagnosis of COVID‐19 is proposed. Due to the usage of capsule network (CapsNet), the authors have succeeded in removing the drawbacks found in the CNN‐based decision support system for the detection of COVID‐19. Through simulation results, it is found that VGG‐CapsNet has performed better than the CNN‐CapsNet model for the diagnosis of COVID‐19. The proposed VGG‐CapsNet‐based system has shown 97% accuracy for COVID‐19 versus non‐COVID‐19 classification, and 92% accuracy for COVID‐19 versus normal versus viral pneumonia classification. Proposed VGG‐CapsNet‐based system available at https://github.com/shamiktiwari/COVID19_Xray can be used to detect the existence of COVID‐19 virus in the human body through chest radiographic images.

Alzheimer’s disease (AD) is an old-age disease that comes in different stages and directly affects the different regions of the brain. The research into the detection of AD and its stages has new advancements in terms of single-modality and multimodality approaches. However, sustainable techniques for the detection of AD and its stages still require a greater extent of research. In this study, a multimodal image-fusion method is initially proposed for the fusion of two different modalities, i.e., PET (Positron Emission Tomography) and MRI (Magnetic Resonance Imaging). Further, the features obtained from fused and non-fused biomarkers are passed to the ensemble classifier with a Random Forest-based feature selection strategy. Three classes of Alzheimer’s disease are used in this work, namely AD, MCI (Mild Cognitive Impairment) and CN (Cognitive Normal). In the resulting analysis, the Binary classifications, i.e., AD vs. CN and MCI vs. CN, attained an accuracy (A_cc) of 99% in both cases. The class AD vs. MCI detection achieved an adequate accuracy (A_cc) of 91%. Furthermore, the Multi Class classification, i.e., AD vs. MCI vs. CN, achieved 96% (A_cc).