Diagnosis of Malignant Melanoma of Skin Cancer Types

Alasadi, Abbas H. Hassin; Alsafy, Baidaa M.

doi:10.9781/ijimai.2017.458

Cited by 9 publications

(3 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It provides a boost to the sensitivity of the information in images and presents enhanced input to carry out other procedures. In skin cancer recognition the research utilized an anisotropic diffusion filter [ 34 , 35 ], median filter [ 35 , 36 ], Dull Razor [ 37 , 38 , 39 , 40 ], Adam Huang algorithm [ 41 ], Wiener filter [ 42 ], Bilateral filter [ 43 ] and Gaussian and Gaussian Blur filter [ 44 , 45 ], Z-score transformation [ 46 ], contrast-limited adaptive histogram equalization [ 47 , 48 ], adaptive histogram equalization [ 49 ], global-local contrast stretching [ 50 ], color constancy with shades of gray [ 51 ], adaptive gamma correction [ 52 ], gamma and correction [ 53 ], etc., for enhancement and noise removal in the skin recognition process.…”

Section: Skin Cancer Recognition and Classification Systemmentioning

confidence: 99%

Skin Lesion Analysis and Cancer Detection Based on Machine/Deep Learning Techniques: A Comprehensive Survey

Zafar

Sharif

et al. 2023

Life

View full text Add to dashboard Cite

The skin is the human body’s largest organ and its cancer is considered among the most dangerous kinds of cancer. Various pathological variations in the human body can cause abnormal cell growth due to genetic disorders. These changes in human skin cells are very dangerous. Skin cancer slowly develops over further parts of the body and because of the high mortality rate of skin cancer, early diagnosis is essential. The visual checkup and the manual examination of the skin lesions are very tricky for the determination of skin cancer. Considering these concerns, numerous early recognition approaches have been proposed for skin cancer. With the fast progression in computer-aided diagnosis systems, a variety of deep learning, machine learning, and computer vision approaches were merged for the determination of medical samples and uncommon skin lesion samples. This research provides an extensive literature review of the methodologies, techniques, and approaches applied for the examination of skin lesions to date. This survey includes preprocessing, segmentation, feature extraction, selection, and classification approaches for skin cancer recognition. The results of these approaches are very impressive but still, some challenges occur in the analysis of skin lesions because of complex and rare features. Hence, the main objective is to examine the existing techniques utilized in the discovery of skin cancer by finding the obstacle that helps researchers contribute to future research.

show abstract

Section: Skin Cancer Recognition and Classification Systemmentioning

confidence: 99%

Skin Lesion Analysis and Cancer Detection Based on Machine/Deep Learning Techniques: A Comprehensive Survey

Zafar

Sharif

et al. 2023

Life

View full text Add to dashboard Cite

show abstract

“…This process is commonly known as metastasis. Malignant melanoma has four major subtypes: superficial spreading melanoma, nodular melanoma, lentigo malignant melanoma, and acral lentiginous melanoma [2]. The acral lentiginous melanoma is most commonly found in people with darker skin, such as Hispanic, African, and Asian ancestries.…”

Section: Introductionmentioning

confidence: 99%

Melanoma Classification from Dermoscopy Images Using Ensemble of Convolutional Neural Networks

et al. 2021

View full text Add to dashboard Cite

Human skin is the most exposed part of the human body that needs constant protection and care from heat, light, dust, and direct exposure to other harmful radiation, such as UV rays. Skin cancer is one of the dangerous diseases found in humans. Melanoma is a form of skin cancer that begins in the cells (melanocytes) that control the pigment in human skin. Early detection and diagnosis of skin cancer, such as melanoma, is necessary to reduce the death rate due to skin cancer. In this paper, the classification of acral lentiginous melanoma, a type of melanoma with benign nevi, is being carried out. The proposed stacked ensemble method for melanoma classification uses different pre-trained models, such as Xception, Inceptionv3, InceptionResNet-V2, DenseNet121, and DenseNet201, by employing the concept of transfer learning and fine-tuning. The selection of pre-trained CNN architectures for transfer learning is based on models having the highest top-1 and top-5 accuracies on ImageNet. A novel stacked ensemble-based framework is presented to improve the generalizability and increase robustness by fusing fine-tuned pre-trained CNN models for acral lentiginous melanoma classification. The performance of the proposed method is evaluated by experimenting on a Figshare benchmark dataset. The impact of applying different augmentation techniques has also been analyzed through extensive experimentations. The results confirm that the proposed method outperforms state-of-the-art techniques and achieves an accuracy of 97.93%.

show abstract

“…Skin cancer is currently one of the fastestgrowing tumors worldwide, and melanoma is the most life-threatening form of skin cancer [1][2]. Computer-aided diagnostics of skin disease is one of the more problematic difficulties in medical image processing [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Deep CNN Based Skin Lesion Image Denoising and Segmentation using Active Contour Method

Abdullah¹,

Abduljaleel

2019

ETJ

View full text Add to dashboard Cite

Diagnosis of Malignant Melanoma of Skin Cancer Types

Cited by 9 publications

References 1 publication

Skin Lesion Analysis and Cancer Detection Based on Machine/Deep Learning Techniques: A Comprehensive Survey

Skin Lesion Analysis and Cancer Detection Based on Machine/Deep Learning Techniques: A Comprehensive Survey

Melanoma Classification from Dermoscopy Images Using Ensemble of Convolutional Neural Networks

Deep CNN Based Skin Lesion Image Denoising and Segmentation using Active Contour Method

Contact Info

Product

Resources

About