• Dr. Amol Potgantwar
• Kajal Patil
• Komal Bhadane
• Poonam Jadhav
• Sunidhi Jain

This study explores various deep learning architectures and techniques that have been developed and applied to breast cancer detection. Convolutional neural networks (CNNs) have been widely utilized due to their ability to automatically extract relevant features from mammograms or other medical images. The process of breast cancer detection using deep learning typically involves several stages, including image preprocessing, feature extraction, and classification. Preprocessing techniques are employed to enhance image quality, reduce noise, and normalize image characteristics. Feature extraction techniques aim to identify informative patterns and structures within the images, enabling the deep learning model to differentiate between benign and malignant tumors. Classification algorithms, such as fully connected layers or support vector machines, are then employed to classify the extracted features into respective categories. The performance of deep learning models for breast cancer detection is evaluated using metrics such as sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC-ROC). deep learning techniques offer promising avenues for breast cancer classification and detection, providing an opportunity to improve accuracy and efficiency in early diagnosis. Continued research and development in this field are crucial to overcome the challenges and further enhance the performance of deep learning models, ultimately leading to improved patient care and outcomes in breast cancer detection and management.

Breast cancer, classification, detection, deep learning, convolutional neural networks, image preprocessing.

"BREAST CANCER CLASSIFICATION AND DETECTION USING DEEP LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.g260-g263, May-2023, Available :http://www.jetir.org/papers/JETIR2305636.pdf

"BREAST CANCER CLASSIFICATION AND DETECTION USING DEEP LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppg260-g263, May-2023, Available at : http://www.jetir.org/papers/JETIR2305636.pdf