Breast Cancer (BC) is a major health issue in women of the age group above 45. Identification of BC at an earlier stage is important to reduce the mortality rate. Image-based noninvasive methods are used for early detection and for providing appropriate treatment. Computer-Aided Diagnosis (CAD) schemes can support radiologists in making correct decisions. Computational intelligence paradigms such as Machine Learning (ML) and Deep Learning (DL) have been used in the recent past in CAD systems to accelerate diagnosis. ML techniques are feature driven and require a high amount of domain expertise. However, DL approaches make decisions directly from the image. The current advancement in DL approaches for early diagnosis of BC is the motivation behind this review. This article throws light on various types of CAD approaches used in BC detection and diagnosis. A survey on DL, Transfer Learning, and DL-based CAD approaches for the diagnosis of BC is presented in detail. A comparative study on techniques, datasets, and performance metrics used in state-of-the-art literature in BC diagnosis is also summarized. The proposed work provides a review of recent advancements in DL techniques for enhancing BC diagnosis.

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Timely diagnosis and earlier detection of the dangerous heart conditions will reduce the mortality rate and save life of the patient. For that, it is necessary to automate the classi?cation and prediction of Cardiac Arrhythmia. Raw ECG signal is extracted from the MIT-BIH Arrhythmia database, followed by preprocessing and feature extraction using wavelet transform method. Further the extracted features are used for the classification of four different cardiac arrhythmias such as Bradycardia, Tachycardia, Left and Right Bundle Branch Block. Comparative study on the five different classifiers namely Decision trees, Support Vector Machine (SVM), Discriminant Analysis, k-Nearest Neighbor Classifiers (KNN), Ensemble Classifiers, and its variants are experimented in the proposed work. Among these, the weighted KNN classifier gives higher accuracy (90.3%) and prediction speed (10,000 observations per second) with reduced training time (4.329 seconds), compared with the existing state of the art methods. The prediction speed is 10,000 numbers of observations per second which identifies the heart problem earlier, and so appropriate treatment can be given to the patient. To further improve the classification accuracy, three optimizable classifiers namely Optimizable KNN, optimizable SVM, optimizable ensemble are used for the hyper parameter tunning and weight optimization. The optimizable SVM provides better perform (accuracy 93.4 %) among the three optimizable classifiers as well as the existing state of the art works. Therefore, the proposed work used for earlier Cardiac arrhythmia disease diagnosis and prognosis. Keywords: ECG, Cardiac Arrhythmia, Wavelet Transform, Multi class Classifiers, Decision trees, Support Vector Machine (SVM), Discriminant Analysis, k-Nearest Neighbor Classifiers, Ensemble Classifiers, Optimizable classifier.

Diabetes Mellitus (Diabetes) refers to a chronic disability of the human body to process glucose in the bloodstream. Diabetes is widespread globally and is associated with high healthcare costs and can cause problems such as neuropathy and heart attacks along the line. Accurate diabetes prediction is essential for proper diagnosis and treatment thereafter. Therefore, the proposed work aims to develop a diabetes classification system. This work experiments feature learning through stacked auto encoder and feature fusion of low-level features as well as deep features extracted from the Pima Indians Dataset. The classification process is carried out using a shallow and deep neural network. The proposed feature transformation techniques are evaluated using relevant performance metrics. Using feature fusion, the metric scores are 92.71%, 92.37%, 93.44% and 96.80% for accuracy, sensitivity, specificity, and precision respectively. Henceforth, this study could be used to aid medical practitioners for efficient diabetes diagnosis and to provide treatment at an early stage.