Skin Lesion Detection Using Fuzzy Approach and Classification with CNN

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Author(s)

Prashengit Dhar 1,* Sunanda Guha 2

1. Cox’s Bazar City College, Bangladesh

2. Missouri State University

* Corresponding author.

DOI: https://doi.org/10.5815/ijem.2021.01.02

Received: 2 Dec. 2020 / Revised: 26 Dec. 2020 / Accepted: 20 Jan. 2021 / Published: 8 Feb. 2021

Index Terms

Dermoscopic image, fuzzy logic, segmentation, lesion detection

Abstract

Skin lesion detection at early stage is very effective for patients. As a result, patients can get time for treatment. Moreover, this early detection helps a patient in the long-time survival. However, skin lesion detection from a dermoscopic images is not a general task. Due to inter and intra-observer variations in human interpretations, research on skin lesion detection from dermoscopic images become important. In this paper, we proposed a method to segment and detect lesion of skin from images. The proposed method is based on a set of rules of fuzzy logic approach. Firstly, a set of rules is applied on dermoscopic images. The output is then thresholded. Closing operation as a morphological tool is used on the thresholded image. Then area filtering takes a place which results in the desired output. With respect to different learning models, CNN shows better performance in classifying ISIC and Dermis-dermquest dataset. The system delivers a significant performance, which is remarkable and comparable.

Cite This Paper

Prashengit Dhar, Sunanda Guha, " Skin Lesion Detection Using Fuzzy Approach and Classification with CNN ", International Journal of Engineering and Manufacturing (IJEM), Vol.11, No.1, pp. 11-18, 2021. DOI: 10.5815/ijem.2021.01.02

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