Identification of Melanoma in Dermoscopy Images Using Image Processing Algorithms

H, Asha Gnana Priya; Anitha, J.; Poonima.J, Jacinth

doi:10.1109/iccpcct.2018.8574277

Cited by 21 publications

(8 citation statements)

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“…These include a dependency on diverse, accurately labeled datasets for success, the substantial processing power required by deep learning models, and the inherent lack of interpretability in these models, potentially leading to a less transparent decision-making process. [7] The study employs a hybrid methodology, integrating traditional machine learning methods with deep learning approaches. CNNs, a form of deep learning, are employed to extract features from skin scans.…”

Section: Proposed Systemmentioning

confidence: 99%

Hybrid Features for the Identification and Categorization of Skin Cancer

Nyemeesha V

2024

jes

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The halal food control system is one of the critical aspects that need attention in sustaining the Malaysian halal industry. In practice, the existing halal food control system involves the roles of several stakeholders and is implemented independently according to their respective jurisdictions. No integrated control mechanism coordinates the roles of all stakeholders, thus resulting in fraud cases to easily happen in the industry. To effectively coordinate these roles, an integrated halal food control system must be developed. Therefore, this concept paper aims to discuss the roles of stakeholders involved in the halal food control system and to propose a conceptual halal food stakeholder control system model based on the objectives of the Islamic law framework. Information for this paper is gathered based on a literature review of previous studies related to the halal food control system. The conceptual model is expected to be a reference and guideline for all stakeholders involved and will serve as a theoretical foundation for future research in this area. Additionally, it can enhance consumer trust in Malaysia’s halal certification and support the country’s goal of becoming a leader in the global halal food industry.

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Section: Proposed Systemmentioning

confidence: 99%

Hybrid Features for the Identification and Categorization of Skin Cancer

Nyemeesha V

2024

jes

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“…The performance of any test system is usually measured using the confusion matrix through accuracy, sensitivity, specificity (Anitha, 2018;Ansari, 2017;Ichim, 2018;Jain, 2012;Suganya, 2016;Sumithra, 2015). Test accuracy gives the measurement of the overall correctness of the proposed work, which is calculated as the ratio of the sum of correct clusters to the total clusters and is shown in Equation ( 1 Test Sensitivity gives the percentage of sick people who are correctly identified as sick, which means it is the ratio of the correctly identified sick people and the estimated total sick people: Test Specificity gives the percentage of healthy people who are correctly identified as healthy, which means it is the ratio of the correctly identified healthy people and the estimated total healthy people:…”

Section: System Performance Metricmentioning

confidence: 99%

A Survey on a Skin Disease Detection System

Mamun

Uddin

2021

International Journal of Healthcare Information Systems and Informatics

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Skin diseases are frequent and quite perennial in the world, and in some cases, these lead to cancer. These are curable if detected earlier and treated appropriately. An automated image-based detection system consisting of four main modules: image enhancement, region of interest segmentation, feature extraction, and detection can facilitate early identification of these diseases. Diverse image-based methods incorporating machine learning techniques are developed to diagnose different types of skin diseases. This article focuses on the review of the tools and techniques used in the diagnosis of 28 common skin diseases. Furthermore, it has discussed the available image databases and the evaluation metrics for the performance analysis of various diagnosis systems. This is vital for figuring out the implementation framework as well as the efficacy of the diagnosis methods for the neophyte. Based on the performance accuracy, the state-of-the-art method for the diagnosis of a particular disease is figured out. It also highlights challenges and shows future research directions.

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“…Bajaj et al [31] also discussed a system that uses edge-based segmentation with an active contour method. In another research [32], a bottom-hat filter is used that speeds up the segmentation process. Otsu thresholding method and morphological operations (dilation-erosion) segment the selected portion from the skin.…”

Section: Work (Cnn) With Block Variation Of Local Correlation Coefficients (Bvlc) Basedmentioning

confidence: 99%

Hybrid Methodologies for Segmentation and Classification of Skin Diseases: A Study

Mamun¹,

Uddin²

2021

JCC

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Skin disorders are a serious global health problem for humans. These disorders become dangerous when they grow into the malignant stage. Hence, it is necessary to detect these diseases at their early stage. A mobile-based automated skin disease detection system is vital for detecting skin diseases. This system also offers cure or treatment plans to the affected person through the short message service (SMS) or electronic mail (e-mail). An effective skin disease detection system consists of three processes: segmentation, feature extraction, and classification. Several hybrid methodologies are already developed for the above-mentioned processes for detecting skin diseases at the initial stage. This research gives a standard hybrid framework for detecting skin diseases and highlights some design requirements for achieving high accuracy. Existing state-of-the-art hybrid methods of three processes for detecting skin diseases along with their limitations are also summarized. It also identifies the challenges for developing an effective skin disease detection system and gives future research directions.

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Identification of Melanoma in Dermoscopy Images Using Image Processing Algorithms

Cited by 21 publications

References 1 publication

Hybrid Features for the Identification and Categorization of Skin Cancer

Hybrid Features for the Identification and Categorization of Skin Cancer

A Survey on a Skin Disease Detection System

Hybrid Methodologies for Segmentation and Classification of Skin Diseases: A Study

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