Breast Cancer Detection and Classification Using Microscopic Images and Neural Networks.

Breast Cancer Detection and Classification Using Microscopic Images and Neural Networks.

Prof. Sowmya J1, Apeksh A R2, Farhan Ahamed3, Sujay E 4, Syed Toufiq Pasha 5

1Professor,2Final year student,3Final year student,4 Final year student,5Final Year student

Department of Information Science Engineering. RR Institute of Technology. Bengaluru

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Abstract - Breast cancer remains a major global health concern, particularly among women, where early detection is crucial for effective treatment and increased survival rates. Manual diagnosis through histopathological examination is often time-consuming, prone to human error, and requires expert interpretation. In response to this challenge, we developed a deep learning-based system to automate the detection and classification of breast cancer using microscopic biopsy images, aimed at enhancing diagnostic accuracy and speed in clinical workflows.

Our project employs convolutional neural networks (CNNs) to classify histopathological images into three distinct categories: normal, benign, and malignant. We utilized a publicly available dataset containing high-resolution microscopic images and implemented preprocessing techniques to improve image quality, normalize input dimensions, and optimize feature extraction. The model was developed and trained using TensorFlow with GPU support, allowing for efficient computation and faster training cycles.

Key Words:  Breast cancer, Cancer detection, Cancer classification, Histopathological images, microscopic images, Digital pathology, medical imaging