Dense Pooling layers in Fully Convolutional Network for Skin Lesion Segmentation
This work addresses a critical bottleneck in medical image analysis for skin cancer detection, offering an incremental improvement over existing methods.
The paper tackles the problem of inaccurate border detection in skin lesion segmentation by proposing a new fully convolutional network with dense pooling layers, achieving highly accurate segmentation that outperforms state-of-the-art algorithms on skin lesion datasets.
One of the essential tasks in medical image analysis is segmentation and accurate detection of borders. Lesion segmentation in skin images is an essential step in the computerized detection of skin cancer. However, many of the state-of-the-art segmentation methods have deficiencies in their border detection phase. In this paper, a new class of fully convolutional network is proposed, with new dense pooling layers for segmentation of lesion regions in skin images. This network leads to highly accurate segmentation of lesions on skin lesion datasets which outperforms state-of-the-art algorithms in the skin lesion segmentation.