Paper Title
Enhancing Breast Cancer Diagnosis with Ai-Based Prediction and Detection Model

Abstract
Breast cancer remains one of the most prevalent and life-threatening diseases globally, emphasizing the critical need for accurate and timely detection. This paper presents a comprehensive breast cancer detection system leveraging artificial intelligence (AI) and deep learning techniques. The proposed system integrates a prediction model and a detection model, both utilizing state-of-the-art machine learning algorithms and convolutional neural networks (CNNs). The prediction model utilizes a dataset comprising 569 samples of malignant and benign tumor cells, with 30 real-value features extracted from digitized cell nuclei images and the detection model is built upon the ResNet50V2 architecture, a powerful deep learning model pre-trained on ImageNet. Through a carefully designed pipeline, medical imaging data, including histopathology images indicative of breast cancer, are processed and classified with high accuracy. The model is trained on a curated dataset comprising both cancerous and non-cancerous breast tissue images, enabling robust learning of cancer patterns. To enhance model generalization and performance, the system incorporates data preprocessing techniques and automated machine learning workflows using Python scikit-learn pipelines. Furthermore, rigorous evaluation strategies, including train-test splitting and cross-validation, ensure the reliability and effectiveness of the detection model. The developed breast cancer detection system demonstrates promising results, achieving a remarkable accuracy in identifying cancerous tissue from histopathology images. Moreover, the model offers rapid predictions, potentially reducing diagnosis time from weeks to minutes compared to traditional methods. Overall, this system represents a significant advancement in breast cancer diagnosis, providing clinicians with a reliable tool for early detection and improved patient outcomes. Keywords - Breast cancer, AI-based prediction, Detection model, Convolutional Neural Networks, ResNet50V2, Histopathology images, Machine learning, Data preprocessing, Automated pipelines, Early diagnosis.