Breast Cancer Detection Using Transfer Learning Contribution

Abstract

: Breast cancer remains one of the most critical health concerns for women, and early detection plays a major role in improving survival rates. With the rapid growth of deep learning techniques, computer-based systems have become increasingly effective at analyzing and classifying medical images. In this work, deep learning particularly transfer learning is used to identify breast cancer from histopathological images. Transfer learning allows us to take advantage of powerful models that have already been trained on vast image datasets and adapt them to our specific task. For this study, three well-known pre-trained convolutional neural network (CNN) architectures InceptionV3, EfficientNet, and ResNet50 were evaluated to determine which performs best for breast cancer classification. Each model was fine-tuned using our collection of breast tissue images. During experimentation, we noticed that some models had difficulty learning the necessary patterns for correct classification. Among them, ResNet50 stood out by producing more reliable and accurate predictions. The workflow followed several essential stages: images were preprocessed and resized according to each model’s input requirements, and data augmentation was applied to increase variety and prevent overfitt ing. After preparing the dataset, the models were trained and assessed using standard evaluation metrics, helping us compare their strengths and weaknesses. The results clearly indicate that the choice of architecture has a major impact on performance, esp ecially in medical image analysis. ResNet50’s deeper network and skip connections enabled it to capture fine-grained features within the images, leading to improved accuracy. This demonstrates the strong potential of deep learning techniques in healthcare. Beyond building an effective classifier, this project also emphasizes how artificial intelligence can support medical professionals in diagnosis. A dependable system can minimize human error, assist doctors in decision-making, and accelerate the detection process particularly in regions where experienced pathologists may be scarce. Overall, this work shows that transfer learning is a practical and efficient method for breast cancer detection and could contribute to better patient outcomes in the future.