Breast cancer is one of the deadly diseases in women that have raised the mortality rate of women. An accurate and early detection of breast cancer using mammogram images is still a complex task. Hence, this article proposes a novel breast cancer detection model, which included five major phases: (a) preprocessing, (b) segmentation, (c) feature extraction, (d) feature selection, and (e) classification. The input mammogram image is initially preprocessed using contrast limited adaptive histogram equalization (CLAHE) and median filtering. The preprocessed image is then subjected to segmentation via the region growing algorithm. Subsequently, geometric features, texture features and gradient features are extracted from the segmented image. Since the length of the feature vector is large, it is essential to select the optimal features. Here, the selection of optimal features is done by a hybrid optimization algorithm. Once the optimal features are selected, they are subjected to the classification process involving the neural network (NN) classifier. As a novelty, the weight of NN is selected optimally to enhance the accuracy of diagnosis (benign and malignant). The optimal feature selection as well as the weight optimization of NN is accomplished by merging the Lion algorithm (LA) and particle swarm optimization (PSO), named as velocity updated lion algorithm (VU‐LA). Finally, a performance‐based evaluation is carried out between VU‐LA and the existing models like, whale optimization algorithm (WOA), gray wolf optimization (GWO), firefly (FF), PSO, and LA.

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Cancer is the most dangerous disease that may cause death and lung cancer is one of them which is more common among all. There are various imaging techniques through which organs can be scanned for diagnosis. Lung canceris a disease that may be caused by unrestrained cell growth in lung. Lung canceris the most common and most dangerous cancer. CT scan can obtain the lung images, but still it has been recognized manually. Manual lung cancer detection is a challenging task because false error rate may lead you to compromise with human’s life. There are lots of researches that has been done in this field but still failed to obtain high precision with minimal error rate. Here the system proposes automatic lung cancer detection using Sobel & Morphological operations that can acquire good precision along with cancer area detection. Sobel is a gradient edge detection technique through which absolute gradient magnitude is computed in the reference of 2D input lung image that is later dilated with morphological operator. The obtained result is liable to attain high precision with less false alarm rate.