Abstract
Breast cancer is one of the most frequent causes of death among women. World Health Organization evaluated that the mortality rate from breast cancer in Pakistan is 26.76% (2018). Chest computed tomography (CT) imaging is a valid diagnostic tool used to diagnose and control the spread of cancer. This study first develops a comprehensive CT-scan image dataset collected from the hospitals of Pakistan. Further, this study explores multiple CNN’s abilities to distinguish between normal imaging and CT breast cancer, For this several state-of-the-art architectures are selected including ResNets, DenseNets, Inception, EfficientNets, VGGNet, etc. We performed experiments on the CT-scan data set and compared the performance of popular convolutional architecture by training models using transfer learning techniques in combination with different image preprocessing techniques. The performance of these models was tested and compared using several metrics including accuracy, F1-score, recall, and precision. Further, the time consumed to converge and the required computational power were also considered to select a model for different use cases. In our experiments, ResNet152V2 achieved the best performance score (90.8% accuracy) and EfficientNetB2 appeared to be the most effective model it achieved a competitive performance score (89.9% accuracy) by utilizing fewer parameters and converged quickly.
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Shehzad, I., Zafar, A. Breast Cancer CT-Scan Image Classification Using Transfer Learning. SN COMPUT. SCI. 4, 789 (2023). https://doi.org/10.1007/s42979-023-02270-6
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DOI: https://doi.org/10.1007/s42979-023-02270-6