Classification of dermoscopic images using soft computing techniques

Maniraj, S P; Sardarmaran, P.

doi:10.1007/s00521-021-05998-5

Cited by 6 publications

(5 citation statements)

References 22 publications

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“…When examining mixtures of white and blue areas, regression constructions is a particularly important problem that has to be addressed. During the Consensus Net Dermoscopy Meeting [4] and [5], the two terminologies in a Blue-White description were merged in order to improve the diagnostic efficacy and the reproducibility of the inter-observer, and two separate histopathologic substrates have also been demonstrated.…”

Section: Related Workmentioning

confidence: 99%

“…This evaluation would comprise a dermoscopic examination and, if necessary, extraction [4]. Line irregularity has been shown to be the most important factor in determining whether or not someone has melanoma [5]. Melanoma has a sporadic border because of its asymmetrical growth rate, the dispersion of melanocytes in various directions, and the retreat of assault in addition to the hereditary shakiness of the sore [6].…”

Section: Introductionmentioning

confidence: 99%

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Methods of Using Artificial Intelligence to Detect the Boundaries of Melanoma

Bhavanisankari¹,

Geetha²,

Sasirekha³

et al. 2023

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Melanoma is the sixth leading cause of death in the United States, with more than nine thousand people succumbing to the disease each year. The earlier melanoma is detected and treated, the longer a person can expect to live after being diagnosed with the disease more analytical developments are still necessary. The skin injury limit anomaly, which addresses the "B"component included in the "ABCD rule,"is regarded as an essential clinical component for the early detection of melanoma. In addition, the blue-white line structure evacuation is another method that helps further strengthen the capability of recognition. In this study, we provide an AI-based localization approach for recognizing skin disease boundary discrepancies. The system was developed for use in dermatological research. The process entails removing the skin disease from the dermoscopic images, identifying the skin lesion, estimating line inconsistency, preparing learning models known as SVM, RF, DT, and KNN gathering move learning to distinguish line anomaly naturally, which ultimately leads to a decision as to whether or not the skin lesion boundary is predictable or not. The approach is very favorable outcomes, with an accuracy rate of 93 percent, a sensitivity rate of 91.6 percent, a specificity rate of 92.8 percent, and an F-score of 95.4 percent, respectively.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Methods of Using Artificial Intelligence to Detect the Boundaries of Melanoma

Bhavanisankari¹,

Geetha²,

Sasirekha³

et al. 2023

View full text Add to dashboard Cite

show abstract

“…The technique of non-linear filtering (median filtering) has the potential to preserve the abovementioned features more than linear filters. The size of the filter window used in this investigation is 21 pixels on each side [22]. Figure 2 shows the preprocessed medianfiltered dermoscopic image.…”

Section: Preprocessingmentioning

confidence: 99%

Design of inception architecture for skin melanoma classification

Manikandan

Karthikeyan

2023

IJEECS

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The number of people diagnosed with skin cancer is increasing sharply. Both invasive and non-invasive methods of examination may be used to investigate it. However, the invasive method is more difficult for the patient because samples must be taken from the lesion itself, or the whole lesion must be cut out. It also requires more time and cost. To avoid invasive procedures, computer-based analysis and diagnosis have the potential to increase diagnostic accuracy and turnaround time. This study develops a unique discriminative deep learning architecture (DDLA) for dermoscopic image classification (DIC), called DDLA-DIC, which uses the concept of inception. Using this concept, the proposed DDLA-DIC system is designed wider and deeper and the network learns from various spatial patterns. The proposed DDLA-DIC system can extract image characteristics from dermoscopic images for skin cancer diagnosis in an effective and efficient way. The proposed DDLA-DIC system is evaluated by utilizing the dermoscopic images from the PH2 database, and the obtained classification results are based on a random split approach. The simulation results indicate that the framework has a great deal of potential with 99.79% accuracy.

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“…Median filter (MF) is employed in this study due to its advantages over other filters. It is an order statistic filter which operates on the data inside a window of size (2m+1, 2m+1) [23]. It is defined in Eq.…”

Section: Image Preprocessingmentioning

confidence: 99%

Efficient Feature Based Melanoma Skin Image Classification Using Machine Learning Approaches

Pitchiah¹,

Rajamanickam²

2022

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Skin cancer is the most prevalent and deadliest kind of cancer. Melanoma is the most dangerous type of skin cancer, but it can be detected earlier and successfully treated. The dermoscopic image classification using Machine Learning (ML) approaches in identifying melanoma is increased over the last two decades. The proposed classification system involves three stages. Initially, the pre-processing employs the median filter and thresholding approach aids to remove the hairs and unwanted noise. Then, the shape components, Asymmetry, Border Irregularity, Colour and Dermoscopic structure (ABCD) rule, and Grey Level Co-occurrence Matrix (GLCM) features are utilized to extract the skin lesion region. After that, the K-Nearest Neighbor (KNN), Random Forest (RF), and Support Vector Machine (SVM) classifiers are employed to perform melanoma skin classification from the skin lesion. The dermoscopic skin images used in this study are obtained from the PH2 database. Finally, the SVM classifier outperformed the other classifiers in identifying melanoma skin cancer, providing 94.81% efficiency.

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Classification of dermoscopic images using soft computing techniques

Cited by 6 publications

References 22 publications

Methods of Using Artificial Intelligence to Detect the Boundaries of Melanoma

Methods of Using Artificial Intelligence to Detect the Boundaries of Melanoma

Design of inception architecture for skin melanoma classification

Efficient Feature Based Melanoma Skin Image Classification Using Machine Learning Approaches

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