| dc.description.abstract | Deep Learning has emerged as a highly efficient and powerful tool in the field of computer vision, with significant applications in the medical domain. This dissertation focuses on presenting novel Deep Learning approaches for electrocardiogram (ECG) classification and skin lesion segmentation.
For ECG classification, a new model was thoroughly investigated that outperforms existing methods. Impressively, it achieved an accuracy of 98.5% on the PhysioNet dataset MIT-BIH, 98.28% on the PTB database, and an F1 score of approximately 86.71% on the PhysioNet Challenge 2017 dataset.
In the context of skin cancer prediction, the conventional approach of visual examination by dermatologists faces its limitations, such as low accuracy, extensive time consumption, and reliance on human factors and expertise. To overcome these challenges, we propose a novel lightweight segmentation model, mobile anti-aliasing attention U-Net. Experimental results on the 2018 Skin Image Collaboration (ISIC) and PH2 datasets demonstrate that our approach not only requires fewer parameters, but also outperforms several state-of-the-art segmentation methods.
In summary, our Deep Learning approaches exhibit superiority in medical applications and hold the potential for practical implementation in real devices due to their efficient inference capabilities. The outcomes of this research contribute to the progress in this field and pave the way for further research and application of these methods in medical settings. | en_US |