Use of Transfer Learning and Wavelet Transform for Breast Cancer Detection
This work addresses the problem of improving early breast cancer detection accuracy for radiologists, but it appears incremental as it combines existing techniques.
The paper tackled breast cancer detection from mammograms by introducing segmentation and wavelet transform as pre-processing to enhance features for transfer learning in neural networks, resulting in significantly increased accuracy on the Mini-MIAS dataset.
Breast cancer is one of the most common cause of deaths among women. Mammography is a widely used imaging modality that can be used for cancer detection in its early stages. Deep learning is widely used for the detection of cancerous masses in the images obtained via mammography. The need to improve accuracy remains constant due to the sensitive nature of the datasets so we introduce segmentation and wavelet transform to enhance the important features in the image scans. Our proposed system aids the radiologist in the screening phase of cancer detection by using a combination of segmentation and wavelet transforms as pre-processing augmentation that leads to transfer learning in neural networks. The proposed system with these pre-processing techniques significantly increases the accuracy of detection on Mini-MIAS.