使用机器学习和信息论的黑色素瘤诊断新范式

Kailash Chandra Giri, Mayank Patel, Amit Sinhal, Diwakar Gautam
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引用次数: 6

摘要

有害黑色素瘤,基本上是最危险的一种表皮恶性肿瘤,只要在早期可修复的范围内得到治疗,就会有一个惊人的结论。早期诊断和仔细摘除可能是治疗黑色素瘤最有力的方法。本研究使用了184张临床皮肤损伤的皮镜照片,其中144张为危险疮,40张为温和疮,利用图像预处理和分割技术将黑色素瘤与温和的色素疮区分开。培养了基于Otsu和Entropy的图像分割规则,提高了算法的执行力。适当的结果表明,基于Havrda熵和Harris角检测器的黑色素瘤分析方法对Otsu和Harris联合方法具有更大的影响。分离的几何、条纹和阴影高光集被传达,以表征考虑和危险类别之间的黑色素瘤的轮廓限制,并且可以看到基于熵的神经学习方法单独优于基于Otsu的神经学习方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Novel Paradigm of Melanoma Diagnosis Using Machine Learning and Information Theory
Harmful Melanoma, basically the most extremely dangerous sort of epidermis malignancy, has a phenomenal conclusion whenever taken care of inside the reparable early ranges. Early determination and careful extraction is presumably the most vigorous cure of melanoma. This work utilizes a record set of 184 clinical dermatoscopic pictures of skin injuries, in which 144 pictures are of dangerous sores and 40 photos are of the amiable sore, picture pre-handling, and division techniques are utilized to separate melanoma from considerate pigmented sores. Otsu and Entropy fundamentally based picture division rules are cultivated which improves the execution. The appropriate outcomes demonstrate that Havrda Entropy and Harris Corner Detector based melanoma analysis approach accomplish greater affectability concerning Otsu and Harris based joined methodology. The separated geometrical, fringe and shading highlight set is conveyed to characterize an outlining limit among considerate and dangerous classes of melanoma, and it is seen that entropy-based neural learning approach outflanks to Otsu based neural learning approach individually.
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