Breast cancer considered to be a significant health issue among women. Early detection will ensure the treatment is easier and more successful. Recently, numerous methodologies have developed using medical imaging to investigate breast cancer. This research seeks to build a computer-aided diagnostic (CAD) system to interpret mammograms. The first stage of CAD includes preprocessing, Fuzzy c means based segmentation applied to a localized area.In the second stage of the CAD method, the extraction of the feature is carried out using three distinct wavelet families with decomposition level at 4 and 6. The ANN, SVM, and ELM classifiers are used in the final stage to enable accurate classification. This article proposes ELM with the Grasshopper Optimization Algorithm (ELM-GOA) to adjust the weight between the input and hidden layer to obtain maximum performance at the middle layer. This method adopts mammogram enhancement, optimum image segmentation, wavelet-based feature extraction, and grasshopper optimization algorithm based ELM to ameliorating the accuracy and reducing the computational cost.The result shows that ELM-GOA has precision and sensitivity of 100% and 98% respectively. The CAD system can identify tumors with 99.33 % accuracy.

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The most effective treatment for diabetic retinopathy (DR) is the early detection through regular screening, which is critical for a better prognosis. Automatic screening of the images would assist the physicians in diagnosing the condition of patients easily and accurately. This condition searches out for special importance of image processing technology in the way of processing the retinal fundus images. Accordingly, this article plans to develop an automatic DR detection model with the aid of three main stages like (a) image preprocessing, (b) blood vessel segmentation, and (c) classification. The preprocessing phase includes two steps: conversion of RGB to Lab, and contrast enhancement. The Histogram equalization process is done using the contrast enhancement of an image. To the next of preprocessing, the segmentation phase starts with a valuable procedure. It includes (a), thresholding the contrast‐enhanced and filtered images, (b) thresholding the keypoints of contrast‐enhanced and filtered images, and (c) adding both thresholded binary images. Here, the filtering process is performed by proposed adaptive average filtering, where the filter coefficients are tuned or optimized by an improved meta‐heuristic algorithm called fitness probability‐based CSO (FP‐CSO). Finally, the classification part uses Deep CNN, where the improvement is exploited on the convolutional layer, which is optimized by the same improved FP‐CSO. Since the conventional CSO depends on a fitness probability in the improved algorithm, the proposed algorithm termed as FP‐CSO. Finally, valuable comparative and performance analysis has confirmed the effectiveness of the proposed model.

In general, diabetic retinopathy is a hurdle of diabetes that subsists throughout the world. Early detection of this severe disease through computer‐assisted diagnosis tools followed by the right treatment at the right time could control its terrible condition. From the last 2 years, numerous research efforts in this area have been introduced for the automatic detection of diabetic retinopathy with appropriate evaluations. However, there is a large variability in the databases and evaluation criteria used in the literature. Accordingly, this proposal tactics to develop a new contribution to automatic detection of diabetic retinopathy based on four main stages: “(a) image pre‐processing, (b) blood vessels segmentation, (c) feature extraction and dimension reduction, and (d) diabetic retinopathy recognition”. Two steps are used for accomplishing the image pre‐processing, (a) conversion of RGB into green channel image and (b) noise removal by median filtering. Further, the pre‐processed fundus image is subjected to Iterative segmentation‐based blood vessel segmentation. For performing the precise classification of the images, there is a prerequisite to extract the relevant informative features from the segmented blood vessels. Here, the features are extracted using discrete wavelet transform, and gray‐level co‐occurrence matrix. To attain the unique features with different information, the dimension reduction process is applied using principle component analysis. Finally, the Diabetic Retinopathy recognition is performed enabling a hybrid classifier, which merges the beneficial concepts of neural network, and convolutional neural network. As the main novelty, the number of hidden neurons in both neural network and convolutional neural network is optimized by the modified rider optimization algorithm called improvement counter‐based rider optimization algorithm intending to maximize the diagnostic accuracy. Moreover, convolutional neural network takes the transformed form of the segmented blood vessels using Discrete Wavelet Transform as input, and Neural Network takes dimension reduced features as input, and AND‐bit operation of the both classified outputs provides the diagnostic results, whether the corresponding image is normal or abnormal.

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