Advancing Early Diagnosis: Investigating Breast Cancer Cell Segmentation with Deep Learning and Transfer Learning Approaches

Abstract

Breast cancer, a critical global health concern, necessitates accurate and timely diagnosis. This research introduces a novel methodology that harnesses modern technologies, including deep learning and transfer learning, to enhance breast cancer cell segmentation. The study commences with meticulous dataset selection and preprocessing, followed by image segmentation using advanced techniques to differentiate between benign and malignant cells effectively. Two significant algorithms, Convolutional Neural Networks (CNN) and AlexNet, are employed, achieving remarkable classification accuracy of 94.5% and 92.3%, respectively. These models exhibit robust performance in identifying intricate patterns and features in breast cancer cell images, enabling precise diagnoses. Moreover, this study evaluates the models' performance on unseen data, affirming their sustained efficacy in clinical settings. The CNN model excels in accurately classifying and segmenting breast cancer cells, while AlexNet demonstrates transfer learning capabilities, which is particularly advantageous in scenarios with limited data availability. The findings underscore the potential of deep learning and transfer learning techniques in augmenting breast cancer diagnostics, paving the way for more accurate and effective cancer treatments.