Lightweight deep learning model with spatial attention for accurate and efficient breast cancer prediction

Abstract

Breast cancer is a worldwide health crisis that affects a large number of women. Early detection of this disease is critical for determining effective treatment options and improving the chances of positive patient outcomes. In our study, we introduce a new method for detecting breast cancer in its earliest stages using thermographic images and a compact model that can be easily implemented on smartphones. This method is especially useful in areas where medical resources are scarce. We used multiple edge detection techniques like Canny, Roberts and Sobel, and evaluated their effectiveness to improve the accuracy of our model. Our model, which combines MobileNet V2 with a spatial attention mechanism, outperformed other deep learning networks like Inception ResNet and DenseNet121. Furthermore, with an accuracy rate of 98.88%, our proposed model outperformed current state-of-the-art algorithms. These findings point to the potential of our approach for early breast cancer detection and its practical application in resource-limited settings.

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