ViT-MAENB7: An innovative breast cancer diagnosis model from 3D mammograms using advanced segmentation and classification process

•To propose a new 3D mammogram images-derived breast cancer diagnosis model using with the help of adaptive tumor segmentation and new enhanced learning model with optimal feature selection for maximizing the higher efficiency in terms of segmentation and detection.•To segment the tumor parts from the pre-processed images using a new adaptive thresholding with region growing fusion model (AT-RGFM), where the optimization of parameters in binary thresholding and region growing is carried out by cat-rider swarm optimization algorithm (C-RSOA) to maximize the segmentation performance in terms of accuracy and precision.•To implement a novel tumor detection framework called CNN-FS-IFuzzy model with the deep features are extracted from the pooling layer of the convolution neural network (CNN), followed by optimal feature selection (FS) by hybrid C-RSOA, and extends the classification by improved fuzzy (IF)-based learning model using AT-RGFM-based segmented images with three phases like deep feature extraction using CNN, FS by C-RSOA and tumor detection through IFuzzy model, where the major objective of the tumor detection is to maximize the accuracy of detection. Tumors are an important health concern in modern times. Breast cancer is one of the most prevalent causes of death for women. Breast cancer is rapidly becoming the leading cause of mortality among women globally. Early detection of breast cancer allows patients to obtain appropriate therapy, increasing their probability of survival. The adoption of 3-Dimensional (3D) mammography for the medical identification of abnormalities in the breast reduced the number of deaths dramatically. Classification and accurate detection of lumps in the breast in 3D mammography is especially difficult due to factors such as inadequate contrast and normal fluctuations in tissue density. Several Computer-Aided Diagnosis (CAD) solutions are under development to help radiologists accurately classify abnormalities in the breast. In this paper, a breast cancer diagnosis model is implemented to detect breast cancer in cancer patients to prevent death rates. The 3D mammogram images are gathered from the internet. Then, the gathered images are given to the preprocessing phase. The preprocessing is done using a median filter and image scaling method. The purpose of the preprocessing phase is to enhance the quality of the images and remove any noise or artifacts that may interfere with the detection of abnormalities. The median filt