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Alzheimer's disease is a cognitive disorder that threatens human lives. Computer-based diagnosis are recommended for Ad progression prediction. Capsule Networks receive investigators' attention which owes to the capability for the efficient features of the fuse multi-dimensionality and the model's coefficients among the samples. Various deep network models are adopted for node classification and utilization of a complete dataset for constructing the dense network, which is not used for independent testing. In addition, network maintenance and construction benefit matrix decomposition to overcome the demerits. An improved CapsNetwork ( i - C N ) model was proposed for AD diagnosis. The sample data provided among the routing neurons works from the lower level to higher level, i.e. it enhances the prediction accuracy. The model intends to recognize the complete entity by analysing the features of input samples. Capsules are vectors that represent the sample features. The improved network model tries to reduce the computational complexity identified in various existing learning approaches. The under-fitting issues due to smaller dataset are also rectified with the proposed model. Primarily, the learning model is realized by introducing the metric-based feature representation to test independently via the different classification tasks. Appropriate samples are needed to generalize the model without sensitivity and to enhance the performance. Further, designing the i - C N layer poses the probability of realizing the efficient fuse of multi-dimensional data. The model needs to be verified using TADPOLE where mild cognitive impairment (MCI) prediction and the earlier AD diagnosis are achieved. The proposed system gives better performance having an accuracy of 95% and AUC of 98.7%. The outcomes proved that the proposed system enhances flexibility to ensure better performance in the classification. It helps to promote the enhancement of disease diagnosis for deep learning algorithms. [ABSTRACT FROM AUTHOR]

With the increase in the number of cases every year, skin cancer stays as one of the most common cancers worldwide. Although dermatologists have been aided with modern research study in detection of cancer, proper treatment of cancer has been a quite challenging task due to the visual appearance of cells. In this experiment, we have studied segmentation and classification algorithms for early detection of cancerous cells, as early detection may increase survival rate of the affected person. HAM10000 dataset has been utilized in this study which has 10,015 different images into seven different classes. Three different types of segmentation algorithms have been studied in this experiment, U-Net, ResUNet and DeeplabV3+. Among all these, DeeplabV3+ appears to outperform the rest of the algorithms giving an overall accuracy of 96.21%, precision and recall of 93.26% and 93% respectively. Few pre-trained models namely ResNet50, ResNet152, SqueezeNet1.1 and DenseNet121 have been utilized for classification of skin cancer. As per the results obtained from the experiment, ResNet152 outperforms the rest of the three pre-trained models with an overall training and validation accuracy of 81.75% and 78.51% respectively. [ABSTRACT FROM AUTHOR]