卷积神经网络
计算机科学
块(置换群论)
人工智能
频道(广播)
模式识别(心理学)
人体皮肤
卷积(计算机科学)
皮肤损伤
人工神经网络
皮肤病科
医学
生物
电信
数学
遗传学
几何学
作者
R Karthik,Tejas Sunil Vaichole,Sanika Kulkarni,Ojaswa Yadav,Faiz Ahmed Khan
标识
DOI:10.1016/j.bspc.2021.103406
摘要
The primary layer of protection for vital organs in the human body is the skin. It functions as a barrier to protect our internal organs from different sources. However, infections caused by fungus, viruses, or even dust can damage the skin. A tiny lesion on the skin can grow into something that can cause serious health problems. A good diagnosis can help the person suffering from a skin disease to recover quickly. This research aims to develop a system for detecting skin diseases using a Convolution Neural Network (CNN). The proposed model named Eff2Net is built on EfficientNetV2 in conjunction with the Efficient Channel Attention (ECA) block. This research attempts to replace the standard Squeeze and Excitation (SE) block in the EfficientNetV2 architecture with the ECA block. By doing so, it was observed that there was a significant drop in the total number of trainable parameters. The proposed CNN learnt around 16 M parameters to classify the disease, which is comparatively less than the existing deep learning approaches reported in the literature. This skin disease classification was performed on four classes: acne, actinic keratosis (AK), melanoma, and psoriasis. The model achieved an overall testing accuracy of 84.70%.
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