Melanoma Detection from Dermatoscopic Images using Deep Convolutional Neural Networks

Naronglerdrit, Prasitthichai; Mporas, Iosif; Paraskevas, Michael; Kapoulas, Vaggelis

doi:10.1109/bia50171.2020.9244493

Cited by 2 publications

(2 citation statements)

References 26 publications

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“…Naronglerdrit et al [5], offered an experimental study of diverse pre-trained transfer learning models for the classification of malignant skin lesions. Using various pre-trained models, the authors carried out tasks like pre-processing (hair removal), lesion segmentation, batch normalization, and melanoma classification.…”

Section: Related Workmentioning

confidence: 99%

“…Table 5 provides a comparative evaluation of the proposed model with other methods. The experimental study provided by Naronglerdrit et al[5] over the HAM10000 dataset provided an accuracy of 97.12%, but less recall value of 85.18% using the ResNet-101 deep learning model indicates the model suffers from the problem of overfitting. Categorical accuracy of the MobileNet model with data augmentation proposed by Chaturvedi et al[7] provided an overall accuracy of 83.15%, precision of 89%, recall of 83%, and F1-score of 83% only.…”

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Skin Lesion Classification towards Melanoma Detection Using EfficientNetB3

Salian

Sawarkar

2023

Adv. technol. innov.

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The rise of incidences of melanoma skin cancer is a global health problem. Skin cancer, if diagnosed at an early stage, enhances the chances of a patient’s survival. Building an automated and effective melanoma classification system is the need of the hour. In this paper, an automated computer-based diagnostic system for melanoma skin lesion classification is presented using fine-tuned EfficientNetB3 model over ISIC 2017 dataset. To improve classification results, an automated image pre-processing phase is incorporated in this study, it can effectively remove noise artifacts such as hair structures and ink markers from dermoscopic images. Comparative analyses of various advanced models like ResNet50, InceptionV3, InceptionResNetV2, and EfficientNetB0-B2 are conducted to corroborate the performance of the proposed model. The proposed system also addressed the issue of model overfitting and achieved a precision of 88.00%, an accuracy of 88.13%, recall of 88%, and F1-score of 88%.

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confidence: 99%

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Skin Lesion Classification towards Melanoma Detection Using EfficientNetB3

Salian

Sawarkar

2023

Adv. technol. innov.

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Systematic Review of Deep Learning Techniques in Skin Cancer Detection

Magalhaes,

Mendes,

Vardasca

2024

BioMedInformatics

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Skin cancer is a serious health condition, as it can locally evolve into disfiguring states or metastasize to different tissues. Early detection of this disease is critical because it increases the effectiveness of treatment, which contributes to improved patient prognosis and reduced healthcare costs. Visual assessment and histopathological examination are the gold standards for diagnosing these types of lesions. Nevertheless, these processes are strongly dependent on dermatologists’ experience, with excision advised only when cancer is suspected by a physician. Multiple approaches have surfed over the last few years, particularly those based on deep learning (DL) strategies, with the goal of assisting medical professionals in the diagnosis process and ultimately diminishing diagnostic uncertainty. This systematic review focused on the analysis of relevant studies based on DL applications for skin cancer diagnosis. The qualitative assessment included 164 records relevant to the topic. The AlexNet, ResNet-50, VGG-16, and GoogLeNet architectures are considered the top choices for obtaining the best classification results, and multiclassification approaches are the current trend. Public databases are considered key elements in this area and should be maintained and improved to facilitate scientific research.

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Melanoma Detection from Dermatoscopic Images using Deep Convolutional Neural Networks

Cited by 2 publications

References 26 publications

Skin Lesion Classification towards Melanoma Detection Using EfficientNetB3

Skin Lesion Classification towards Melanoma Detection Using EfficientNetB3

Systematic Review of Deep Learning Techniques in Skin Cancer Detection

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