{"title":"使用基于模糊的 SegNet 模型和归一化堆叠 LSTM 网络准确检测黑色素瘤皮肤癌","authors":"Woothukadu Thirumaran Chembian, K. Sankar, Seerangan Koteeswaran, Kandasamy Thinakaran, Periyannan Raman","doi":"10.11591/ijeecs.v35.i1.pp323-334","DOIUrl":null,"url":null,"abstract":"Early detection of melanoma skin cancer (MSC) is critical in order to prevent deaths from fatal skin cancer. Even though the modern research methods are effective in identifying and detecting skin cancer, it is a challenging task due to a higher level of color similarity between melanoma non-affected areas and affected areas, and a lower contrast between the skin portions and melanoma moles. For highlighting the aforementioned problems, an efficient automated system is proposed for an early diagnosis of MSC. Firstly, dermoscopic images are collected from two benchmark datasets namely, international skin imaging collaboration (ISIC)-2017 and PH2. Next, skin lesions are segmented from dermoscopic images by implementing a fuzzy based SegNet model which is a combination of both deep fuzzy clustering algorithm and the SegNet model. Then, hybrid feature extraction (ResNet-50 model and local tri-directional pattern (LTriDP) descriptor) is performed to capture the features from segmented skin lesions. These features are given into the normalized stacked long short-term memory (LSTM) network to categorize the classes of skin lesions. The empirical evaluation reveals that the proposed normalized stacked LSTM network achieves 98.98% and 98.97% of accuracy respectively on the ISIC2017 and PH2 datasets, and these outcomes are more impressive than those of the conventional detection models.","PeriodicalId":13480,"journal":{"name":"Indonesian Journal of Electrical Engineering and Computer Science","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Accurate detection of melanoma skin cancer using fuzzy based SegNet model and normalized stacked LSTM network\",\"authors\":\"Woothukadu Thirumaran Chembian, K. Sankar, Seerangan Koteeswaran, Kandasamy Thinakaran, Periyannan Raman\",\"doi\":\"10.11591/ijeecs.v35.i1.pp323-334\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Early detection of melanoma skin cancer (MSC) is critical in order to prevent deaths from fatal skin cancer. Even though the modern research methods are effective in identifying and detecting skin cancer, it is a challenging task due to a higher level of color similarity between melanoma non-affected areas and affected areas, and a lower contrast between the skin portions and melanoma moles. For highlighting the aforementioned problems, an efficient automated system is proposed for an early diagnosis of MSC. Firstly, dermoscopic images are collected from two benchmark datasets namely, international skin imaging collaboration (ISIC)-2017 and PH2. Next, skin lesions are segmented from dermoscopic images by implementing a fuzzy based SegNet model which is a combination of both deep fuzzy clustering algorithm and the SegNet model. Then, hybrid feature extraction (ResNet-50 model and local tri-directional pattern (LTriDP) descriptor) is performed to capture the features from segmented skin lesions. These features are given into the normalized stacked long short-term memory (LSTM) network to categorize the classes of skin lesions. The empirical evaluation reveals that the proposed normalized stacked LSTM network achieves 98.98% and 98.97% of accuracy respectively on the ISIC2017 and PH2 datasets, and these outcomes are more impressive than those of the conventional detection models.\",\"PeriodicalId\":13480,\"journal\":{\"name\":\"Indonesian Journal of Electrical Engineering and Computer Science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Indonesian Journal of Electrical Engineering and Computer Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.11591/ijeecs.v35.i1.pp323-334\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Mathematics\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Indonesian Journal of Electrical Engineering and Computer Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.11591/ijeecs.v35.i1.pp323-334","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Mathematics","Score":null,"Total":0}
Accurate detection of melanoma skin cancer using fuzzy based SegNet model and normalized stacked LSTM network
Early detection of melanoma skin cancer (MSC) is critical in order to prevent deaths from fatal skin cancer. Even though the modern research methods are effective in identifying and detecting skin cancer, it is a challenging task due to a higher level of color similarity between melanoma non-affected areas and affected areas, and a lower contrast between the skin portions and melanoma moles. For highlighting the aforementioned problems, an efficient automated system is proposed for an early diagnosis of MSC. Firstly, dermoscopic images are collected from two benchmark datasets namely, international skin imaging collaboration (ISIC)-2017 and PH2. Next, skin lesions are segmented from dermoscopic images by implementing a fuzzy based SegNet model which is a combination of both deep fuzzy clustering algorithm and the SegNet model. Then, hybrid feature extraction (ResNet-50 model and local tri-directional pattern (LTriDP) descriptor) is performed to capture the features from segmented skin lesions. These features are given into the normalized stacked long short-term memory (LSTM) network to categorize the classes of skin lesions. The empirical evaluation reveals that the proposed normalized stacked LSTM network achieves 98.98% and 98.97% of accuracy respectively on the ISIC2017 and PH2 datasets, and these outcomes are more impressive than those of the conventional detection models.
期刊介绍:
The aim of Indonesian Journal of Electrical Engineering and Computer Science (formerly TELKOMNIKA Indonesian Journal of Electrical Engineering) is to publish high-quality articles dedicated to all aspects of the latest outstanding developments in the field of electrical engineering. Its scope encompasses the applications of Telecommunication and Information Technology, Applied Computing and Computer, Instrumentation and Control, Electrical (Power), Electronics Engineering and Informatics which covers, but not limited to, the following scope: Signal Processing[...] Electronics[...] Electrical[...] Telecommunication[...] Instrumentation & Control[...] Computing and Informatics[...]