An optimal method for melanoma detection from dermoscopy images using reinforcement learning and support vector machine optimized by enhanced fish migration optimization algorithm

Liu, Qianqian; Kawashima, Hiroto; Rezaei sofla, Asad

doi:10.1016/j.heliyon.2023.e21118

Cited by 7 publications

(2 citation statements)

References 46 publications

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“…The coefficient 𝛾 is used for controlling the scale of KL-divergence. Further, the graph regularization 𝜐 𝑜 is calculated by using 𝑇 𝑠 and 𝐾 𝑠 , and is mathematically depicted in (2).…”

Section: Dataset Descriptionmentioning

confidence: 99%

“…A high exposure to ultraviolet radiation causes this damage, and leads to unregulated and excessive growth of cells. The benign skin cancers (cysts, seborrheic keratosis, dermatofibromas, cherry angiomas, skin tags, and pyogenic granulomas) do not spread, whereas malignant skin cancers are uncontrollable and spread throughout the human body [2], [3]. There is a chance of curing  ISSN: 2502-4752 Indonesian J Elec Eng & Comp Sci, Vol.…”

Section: Introductionmentioning

confidence: 99%

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Accurate detection of melanoma skin cancer using fuzzy based SegNet model and normalized stacked LSTM network

Chembian,

Sankar,

Koteeswaran

et al. 2024

IJEECS

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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.

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Section: Dataset Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Accurate detection of melanoma skin cancer using fuzzy based SegNet model and normalized stacked LSTM network

Chembian,

Sankar,

Koteeswaran

et al. 2024

IJEECS

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A data‐driven Bayesian approach for optimal dynamic product transitions

Flores‐Tlacuahuac,

Fuentes‐Cortés

2024

AIChE Journal

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In the processing industry, dynamic product transitions are essential for achieving high product quality, minimizing the use of raw materials and energy and reducing production costs. However, optimizing dynamic product transitions is a challenging task due to the complex dynamics of the process and the uncertainty in the measurements. In this article, a data‐driven Bayesian approach for optimal dynamic product transitions is proposed. The proposed approach is based on a dynamic optimization problem that is solved using a Bayesian optimization algorithm. One of the advantages of this approach for process optimization tasks is that it does not require a first‐principles dynamic mathematical model for drawing optimal solutions. The approach is applied to three case studies, and the results are comparable in performance quality with those obtained using a traditional gradient‐based optimization approach. The results show that the proposed approach is able to find optimal transition trajectories that meet the product composition requirements using smooth control actions. The approach is also able to cope with noisy measurements, which is an important feature in real‐world applications. The proposed approach has several advantages over traditional optimization approaches, including being data driven, able to cope with noisy measurements, computationally efficient, and it requires modest computational effort. Complex online optimal control problems can benefit from adopting a data‐driven Bayesian optimization scheme.

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Application of deep reinforcement learning in various image processing tasks: a survey

Tadesse,

Kebede,

Debele

et al. 2024

Evolving Systems

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An optimal method for melanoma detection from dermoscopy images using reinforcement learning and support vector machine optimized by enhanced fish migration optimization algorithm

Cited by 7 publications

References 46 publications

Accurate detection of melanoma skin cancer using fuzzy based SegNet model and normalized stacked LSTM network

Accurate detection of melanoma skin cancer using fuzzy based SegNet model and normalized stacked LSTM network

A data‐driven Bayesian approach for optimal dynamic product transitions

Application of deep reinforcement learning in various image processing tasks: a survey

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