使用预训练的Inception V3检测白癜风皮肤病的基于深度学习的模型

IF 1.3 Q3 ENGINEERING, MULTIDISCIPLINARY
Shagun Sharma, Kalpna Guleria, Sushil Kumar, S. Tiwari
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引用次数: 0

摘要

皮肤病是世界各地普遍存在的问题。皮肤病种类繁多,如皮肤癌症、寻常型、鱼鳞病和湿疹。白癜风是一种皮肤病,可发生在身体的任何部位,包括口腔内部。这种皮肤会对人体产生巨大的负面影响,包括记忆力问题、高血压和心理健康问题。传统上,皮肤科医生使用活检、血液测试和贴片测试来识别皮肤疾病的存在,并为患者提供药物。然而,由于黄斑变为斑块,这些治疗并不总是能带来效果。已经开发了各种机器学习(ML)和深度学习(DL)模型来早期识别黄斑,以避免治疗延迟。这项工作实现了一个基于DL的模型,用于预测和分类健康皮肤中的白癜风皮肤病。使用预先训练的Inception V3模型提取图像中的特征,并替换每个分类器,即朴素贝叶斯、卷积神经网络(CNN)、随机森林和决策树。结果被确定为Inception V3的准确度、召回率、精密度、曲线下面积(AUC)和F1分数,天真贝叶斯分别为99.5%、0.995、0.995,0.997和0.995。带有CNN的Inception V3的准确率为99.8%,召回率为0.998,精密度为0.998、AUC为1.00,F1得分为0.998。此外,具有随机森林的Inception V3显示出99.9%的准确度、0.999召回率、0.999精密度、1.00 AUC和0.999 F1分,而具有决策树分类器的InceptionV3显示出97.8%的准确度值、0.978召回率、0.977精密度、0.969 AUC和0.977 F1分。结果表明,具有随机森林分类器的Inception V3在准确性、召回率、精确度和F1分数方面表现出色,而对于AUC指标,具有随机林分类器的Incept V3和具有CNN分类器的Incession V3显示出相同的1.00结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Learning based Model for Detection of Vitiligo Skin Disease using Pre-trained Inception V3
Skin diseases are commonly identified problems all over the world. There are various kinds of skin diseases, such as skin cancer, vulgaris, ichthyosis, and eczema. Vitiligo is one of the skin diseases that can occur in any area of the body, including the inner part of the mouth. This type of skin can have immense negative impacts on the human body, involving memory issues, hypertension, and mental health problems. Conventionally, dermatologists use biopsy, blood tests, and patch testing to identify the presence of skin diseases and provide medications to patients. However, these treatments don't always provide results due to the transformation of a macule into a patch. Various machine learning (ML) and deep learning (DL) models have been developed for the early identification of macules to avoid delays in treatments. This work has implemented a DL-based model for predicting and classifying vitiligo skin disease in healthy skin. The features from the images have been extracted using a pre-trained Inception V3 model and substituted for each classifier, namely, naive Bayes, convolutional neural network (CNN), random forest, and decision tree. The results have been determined as accuracy, recall, precision, area under the curve (AUC), and F1-score for Inception V3 with naive Bayes as 99.5%, 0.995, 0.995, 0.997, and 0.995, respectively. The Inception V3 with CNN has achieved 99.8% accuracy, 0.998 recall, 0.998 precision, 1.00 AUC, and 0.998 F1-score. Further, Inception V3 with random forest shows 99.9% accuracy, 0.999 recall, 0.999 precision, 1.00 AUC, and 0.999 F1-score values whereas, Inception V3 with decision tree classifier shows an accuracy value of 97.8%, 0.978 recall, 0.977 precision, 0.969 AUC, and 0.977 F1-score. Results exhibit that Inception V3 with a random forest classifier outperforms in terms of accuracy, recall, precision, and F1-score, whereas for the AUC metric, Inception V3 with a random forest and Inception V3 with CNN have shown the same outcomes of 1.00.
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来源期刊
CiteScore
3.80
自引率
6.20%
发文量
57
审稿时长
20 weeks
期刊介绍: IJMEMS is a peer reviewed international journal aiming on both the theoretical and practical aspects of mathematical, engineering and management sciences. The original, not-previously published, research manuscripts on topics such as the following (but not limited to) will be considered for publication: *Mathematical Sciences- applied mathematics and allied fields, operations research, mathematical statistics. *Engineering Sciences- computer science engineering, mechanical engineering, information technology engineering, civil engineering, aeronautical engineering, industrial engineering, systems engineering, reliability engineering, production engineering. *Management Sciences- engineering management, risk management, business models, supply chain management.
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