Skin lesion classification system using a K-nearest neighbor algorithm

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Hatem, Mustafa Qays
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引用次数: 17

Abstract

One of the most critical steps in medical health is the proper diagnosis of the disease. Dermatology is one of the most volatile and challenging fields in terms of diagnosis. Dermatologists often require further testing, review of the patient’s history, and other data to ensure a proper diagnosis. Therefore, finding a method that can guarantee a proper trusted diagnosis quickly is essential. Several approaches have been developed over the years to facilitate the diagnosis based on machine learning. However, the developed systems lack certain properties, such as high accuracy. This study proposes a system developed in MATLAB that can identify skin lesions and classify them as normal or benign. The classification process is effectuated by implementing the K-nearest neighbor (KNN) approach to differentiate between normal skin and malignant skin lesions that imply pathology. KNN is used because it is time efficient and promises highly accurate results. The accuracy of the system reached 98% in classifying skin lesions.
基于k近邻算法的皮肤病变分类系统
医疗卫生中最关键的步骤之一是对疾病的正确诊断。皮肤科是诊断方面最不稳定和最具挑战性的领域之一。皮肤科医生通常需要进一步的检查,回顾病人的病史和其他数据,以确保正确的诊断。因此,寻找一种能够保证快速做出正确可信诊断的方法至关重要。多年来,已经开发了几种方法来促进基于机器学习的诊断。然而,已开发的系统缺乏某些特性,如高精度。本研究提出了一个用MATLAB开发的系统,该系统可以识别皮肤病变,并将其分类为正常或良性。分类过程通过实施k -最近邻(KNN)方法来区分正常皮肤和暗示病理的恶性皮肤病变。之所以使用KNN,是因为它具有时间效率,并承诺获得高度准确的结果。该系统对皮肤病变的分类准确率达到98%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.20
自引率
4.30%
发文量
567
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