• B. HEMANTH
• B. SAI MANI RAM
• B. CHANDRA MOHAN REDDY
• N. FATHIMA SHRENE SHIFNA

Breast cancer is a global health challenge that demands early detection to improve patient outcomes. In this regard, Convolutional Neural Networks (CNNs) are deep learning techniques that have in recent times exhibited value in diverse medical image analysis applications including breast cancer detection and diagnosis. This paper presents in-depth research on breast cancer identification using CNNs to improve the accuracy and efficiency of diagnosis. In this approach, a dataset was constructed from mammography images collected from different sources which consist of several kinds and stages of breast cancer. Pre-processing methods were used to improve image quality and reduce noise, to enhance the performance of the CNN model. Afterward, a custom CNN architecture designed specifically for identifying breast cancers is created which consists of multiple convolutional & pooling layers followed by fully connected layers for classification. For robust training and evaluation purposes, the dataset is divided into training, validation, and test sets. Extensive experiments were carried out to fine-tune hyperparameters and optimize the CNN architecture to achieve better performances in terms of accuracy, sensitivity, specificity, and computational efficiency

Pancreatic cancer; Diagnosis and staging; EL-SVM; CT; LASSO

"IDENTIFICATION OF BREAST CANCER USING CNN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.f41-f54, April-2024, Available :http://www.jetir.org/papers/JETIR2404506.pdf

"IDENTIFICATION OF BREAST CANCER USING CNN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppf41-f54, April-2024, Available at : http://www.jetir.org/papers/JETIR2404506.pdf