Enhancing Breast Cancer Diagnosis with Deep Learning-Based Image Classification

Breast cancer is a global health concern that has a significant impact on the lives of women. The use of artificial intelligence (AI) in healthcare has shown remarkable promise in improving early disease detection. Deep learning (DL) techniques have advanced in the medical field in recent years, paving the way for more accurate and efficient breast cancer diagnosis.We present a novel approach by using deep learning to analyse mammograms and histopathological images, with the goal of providing timely and reliable results to help healthcare professionals make decisions.The models we proposed have been fine-tuned on a massive dataset of breast cancer images, allowing them to accurately distinguish between malignant and benign cases. In addition, we present a sophisticated ensemble learning technique to improve classification performance even further. Our findings show that there is a significant improvement in the accuracy and efficiency of breast cancer diagnosis. The incorporation of AI and DL technologies in healthcare is set to revolutionise breast cancer detection, ultimately saving lives and reducing the burden on patients and healthcare systems. This paper contributes to this transformative paradigm by introducing innovative DL models tailored for breast cancer diagnosis, demonstrating how these technologies have the potential to have a significant impact on women’s health.