Review of Skin Diseases Classification Methods Using Different Color Phase Models

Ubale, Ashvini V.; Paikrao, Prashant

doi:10.1007/978-981-15-1420-3_92

Lecture Notes in Electrical Engineering

2020

DOI: 10.1007/978-981-15-1420-3_92

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Review of Skin Diseases Classification Methods Using Different Color Phase Models

Ashvini V. Ubale¹,

Prashant Paikrao²

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2024

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“…experience or his or her own subjective assessment, which can lead to errors and, as a result, delays in treatment there is. It's possible that the skin lesions are either primary or secondary [9]. Bulla, cyst, pustule, vesicle, tumour, nodule, discolouration, spots, pimples and plaque becomes main skin lesions, when atrophy, maceration, umbilication, phyla, ulcer, fissure, indurations, excoriation, and erosion are secondary skin lesions.…”

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Deep Long and Short Term Memory with Tunicate Swarm Algorithm for Skin Disease Detection and Classification

Ashwin Narasimha Murthy

2024

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The development and implementation of cost-effective and efficient screening technologies is important. To address these concerns, we have introduced a unique method to detect skin diseases. Each photo is first pre-processed and cropped to pixel size. Six square fields are used to split these pictures into pixels. Techniques for enlarging images, such as rotation, mirroring, and enhancement, are employed to minimize the quantity of parameters needed for further processes. An kernel-weighted fuzzy local information or the C-means clustering model (K-FCM) is used to properly segment cancer-affected regions. Texture and colour features are then extracted. Finally, a deep long-term and short-term memory (DLTM)-based tunicate group algorithm (TSA) is used to detect skin diseases and classify both normal and abnormal classes. The experiment was carried out using MATLAB, and photos were gathered from the Helllev University Hospital in Denmark. According to the comparative analysis results, the proposed DLSTM-TSA outperforms competing products in terms of F-score, sensitivity, and precision.

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Deep Long and Short Term Memory with Tunicate Swarm Algorithm for Skin Disease Detection and Classification

Ashwin Narasimha Murthy

2024

jes

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Review of Skin Diseases Classification Methods Using Different Color Phase Models

Cited by 1 publication

References 17 publications

Deep Long and Short Term Memory with Tunicate Swarm Algorithm for Skin Disease Detection and Classification

Deep Long and Short Term Memory with Tunicate Swarm Algorithm for Skin Disease Detection and Classification

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