Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:3035-3038. doi: 10.1109/EMBC46164.2021.9630512.
Deep learning techniques have been widely employed in semantic segmentation problems, especially in medical image analysis, for understanding image patterns. Skin cancer is a life-threatening problem, whereas timely detection can prevent and reduce the mortality rate. The aim is to segment the lesion area from the skin cancer image to help experts in the process of deeply understanding tissues and cancer cells' formation. Thus, we proposed an improved fully convolutional neural network (FCNN) architecture for lesion segmentation in dermoscopic skin cancer images. The FCNN network consists of multiple feature extraction layers forming a deep framework to obtain a larger vision for generating pixel labels. The novelty of the network lies in the way layers are stacked and the generation of customized weights in each convolutional layer to produce a full resolution feature map. The proposed model was compared with the top four winners of the International Skin Imaging Collaboration (ISIC) challenge using evaluation metrics such as accuracy, Jaccard index, and dice co-efficient. It outperformed the given state-of-the-art methods with higher values of the accuracy and Jaccard index.
深度学习技术已广泛应用于语义分割问题，特别是在医学图像分析中，用于理解图像模式。皮肤癌是一种危及生命的问题，而及时发现可以预防和降低死亡率。其目的是从皮肤癌图像中分割出病变区域，以帮助专家深入了解组织和癌细胞的形成。因此，我们提出了一种改进的全卷积神经网络（FCNN）架构，用于皮肤镜下皮肤癌图像的病变分割。FCNN 网络由多个特征提取层组成，形成一个深层框架，以获得更大的视野，从而生成像素标签。该网络的新颖之处在于层的堆叠方式和每个卷积层中定制权重的生成方式，以产生全分辨率特征图。使用准确率、Jaccard 指数和骰子系数等评估指标，将所提出的模型与国际皮肤成像协作（ISIC）挑战赛的前四名优胜者进行了比较。它的准确率和 Jaccard 指数值更高，优于给定的最新方法。
