Efficient Breast Cancer Detection and Classification Model by Analyzing Mammogram Images Using <scp>ViT</scp> ‐Aided MobileNet With <scp>LSTM</scp> Network Based on Adaptive Segmentation

ABSTRACT One of the prevalent types of malignancy that affects women is called breast cancer. Early identification and treatment of cancer lessen the mortality rate. It is more common in women than men, which significantly increases the death rate of women. Prognosis and early identification are essential for ultimately increasing survival rates. The developed model is used to create effective techniques for detecting cancer in the women's at prior stage utilizing mammogram scans. The breast X‐Ray images is also called as mammogram image. To recognize breast cancer from Mammogram images, an intelligent cancer framework is implemented using deep learning. The images needed for the classification process is collected from standard datasets. Next, the gathered images are provided to the segmented phase. Here, the segmentation is performed using a Weighted Adaptive Mask Region‐based Convolutional Neural Network (WAM‐RCNN), where the weights are optimally tuned using the Enhanced Fennec Fox Optimization (EFFO) algorithm. The segmented outcomes are fed to Vision Transformer‐based MobileNet with Long Short Term Memory (ViT‐MobLSTM) for the “detection and classification of breast cancer.” Finally, the suggested model is correlated with numerous metrics to find the competence of the model. The validation result shows that the developed model outperforms with effective outcomes.