AI-Driven Lungs And Breast Cancer Detection

Computer Engineering

Cancer, particularly lung and breast cancer, remains a significant global health challenge, underscoring the urgency for early detection methods to enhance survival rates. This paper centers on employing artificial intelligence (AI) techniques for the early detection of lung and breast cancer using deep learning and Convolutional Neural Network (CNN) models. This paper presents a comprehensive methodology for the development and evaluation of lung and breast cancer detection models using medical imaging data. Leveraging TensorFlow and a T4 GPU in Google Colab, the methodology navigates through key stages, including dataset collection, data pre-processing, model training, and model evaluation. For lung cancer detection, Chest CT- Scan images were collected and processed using data augmen- tation techniques, leading to the development of a robust Effi- cientNetB0 CNN architecture. The trained model demonstrated high accuracy and generalization ability, with promising results on validation and test datasets. For breast cancer detection, mammography images from the ”cbis-ddsm-breast-cancer-image- dataset” were utilized. A VGG16 CNN architecture was tailored for binary classification, achieving an impressive accuracy on the test dataset.

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