scholarly journals Computer-Aided Diagnosis of Malignant Melanoma Using Gabor-Based Entropic Features and Multilevel Neural Networks

Diagnostics ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. 822
Author(s):  
Samy Bakheet ◽  
Ayoub Al-Hamadi
Keyword(s):  
Malignant Melanoma ◽  
Histogram Equalization ◽  
Skin Lesions ◽  
Cancer Society ◽  
The Public ◽  
Melanoma Detection ◽  
Otsu’S Method ◽  
Local Patterns ◽  
Dermoscopic Image ◽  
Low Dimensional

The American Cancer Society has recently stated that malignant melanoma is the most serious type of skin cancer, and it is almost 100% curable, if it is detected and treated early. In this paper, we present a fully automated neural framework for real-time melanoma detection, where a low-dimensional, computationally inexpensive but highly discriminative descriptor for skin lesions is derived from local patterns of Gabor-based entropic features. The input skin image is first preprocessed by filtering and histogram equalization to reduce noise and enhance image quality. An automatic thresholding by the optimized formula of Otsu’s method is used for segmenting out lesion regions from the surrounding healthy skin regions. Then, an extensive set of optimized Gabor-based features is computed to characterize segmented skin lesions. Finally, the normalized features are fed into a trained Multilevel Neural Network to classify each pigmented skin lesion in a given dermoscopic image as benign or melanoma. The proposed detection methodology is successfully tested and validated on the public PH2 benchmark dataset using 5-cross-validation, achieving 97.5%, 100% and 96.87% in terms of accuracy, sensitivity and specificity, respectively, which demonstrate competitive performance compared with several recent state-of-the-art methods.

scholarly journals Skin Lesion Segmentation in Dermoscopic Images with Combination of YOLO and GrabCut Algorithm

Diagnostics ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 72 ◽  
Author(s):  
Halil Murat Ünver ◽  
Enes Ayan
Keyword(s):  
Skin Lesion ◽  
Automatic Segmentation ◽  
Critical Role ◽  
Skin Lesions ◽  
Lesion Segmentation ◽  
Low Contrast ◽  
Melanoma Detection ◽  
Sensitivity Rate ◽  
Dermoscopic Image ◽  
Grabcut Algorithm

Skin lesion segmentation has a critical role in the early and accurate diagnosis of skin cancer by computerized systems. However, automatic segmentation of skin lesions in dermoscopic images is a challenging task owing to difficulties including artifacts (hairs, gel bubbles, ruler markers), indistinct boundaries, low contrast and varying sizes and shapes of the lesion images. This paper proposes a novel and effective pipeline for skin lesion segmentation in dermoscopic images combining a deep convolutional neural network named as You Only Look Once (YOLO) and the GrabCut algorithm. This method performs lesion segmentation using a dermoscopic image in four steps: 1. Removal of hairs on the lesion, 2. Detection of the lesion location, 3. Segmentation of the lesion area from the background, 4. Post-processing with morphological operators. The method was evaluated on two publicly well-known datasets, that is the PH2 and the ISBI 2017 (Skin Lesion Analysis Towards Melanoma Detection Challenge Dataset). The proposed pipeline model has achieved a 90% sensitivity rate on the ISBI 2017 dataset, outperforming other deep learning-based methods. The method also obtained close results according to the results obtained from other methods in the literature in terms of metrics of accuracy, specificity, Dice coefficient, and Jaccard index.

scholarly journals hybSVM: Bacterial colony optimization algorithm based SVM for malignant melanoma detection

2021 ◽  
Author(s):  
Sümeyya İlkin ◽  
Tuğrul Hakan Gençtürk ◽  
Fidan Kaya Gülağız ◽  
Hikmetcan Özcan ◽  
Mehmet Ali Altuncu ◽  
...  
Keyword(s):  
Malignant Melanoma ◽  
Optimization Algorithm ◽  
Bacterial Colony ◽  
Melanoma Detection

Prevention of Smoking

PEDIATRICS ◽  
1968 ◽  
Vol 41 (6) ◽  
pp. 1143-1144
Author(s):  
Henry P. Staub
Keyword(s):  
Public Health ◽  
American Academy ◽  
Health Hazard ◽  
American Cancer Society ◽  
Executive Board ◽  
Cancer Society ◽  
The Public ◽  
American Academy Of Pediatrics ◽  
Younger Generation

In the Newsletter of January 1, 1968, the American Academy of Pediatrics reported that the executive board strongly endorsed time American Cancer Society's anti-smoking resolution. Personally, I cannot agree with the approach of the resolution to the public health hazard of smoking. If the American Academy of Pediatrics (or for that matter, the American Cancer Society) wanted to back effective measures, an entirely different type of resolution would have been adopted, one that would have put the emphasis On reaciling the younger generation.

AI-Enabled Support System for Melanoma Detection and Classification

2021 ◽  
Vol 10 (4) ◽  
pp. 58-75
Author(s):  
Vivek Sen Saxena ◽  
Prashant Johri ◽  
Avneesh Kumar
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Skin Lesion ◽  
Performance Metrics ◽  
Similarity Index ◽  
Skin Lesions ◽  
Machine Learning Algorithms ◽  
Lesion Area ◽  
Melanoma Detection ◽  
Grabcut Algorithm

Skin lesion melanoma is the deadliest type of cancer. Artificial intelligence provides the power to classify skin lesions as melanoma and non-melanoma. The proposed system for melanoma detection and classification involves four steps: pre-processing, resizing all the images, removing noise and hair from dermoscopic images; image segmentation, identifying the lesion area; feature extraction, extracting features from segmented lesion and classification; and categorizing lesion as malignant (melanoma) and benign (non-melanoma). Modified GrabCut algorithm is employed to generate skin lesion. Segmented lesions are classified using machine learning algorithms such as SVM, k-NN, ANN, and logistic regression and evaluated on performance metrics like accuracy, sensitivity, and specificity. Results are compared with existing systems and achieved higher similarity index and accuracy.

scholarly journals SELECTED APPLICATIONS OF DEEP NEURAL NETWORKS IN SKIN LESION DIAGNOSTIC

2021 ◽  
Vol 11 (4) ◽  
pp. 18-21
Author(s):  
Magdalena Michalska
Keyword(s):  
Neural Networks ◽  
Malignant Melanoma ◽  
Skin Lesion ◽  
Deep Neural Networks ◽  
Skin Lesions ◽  
Multiclass Classification ◽  
Features Selection ◽  
Lesion Segmentation ◽  
Dermatological Diseases ◽  
Segmentation Process

The article provides an overview of selected applications of deep neural networks in the diagnosis of skin lesions from human dermatoscopic images, including many dermatological diseases, including very dangerous malignant melanoma. The lesion segmentation process, features selection and classification was described. Application examples of binary and multiclass classification are given. The described algorithms have been widely used in the diagnosis of skin lesions. The effectiveness, specificity, and accuracy of classifiers were compared and analysed based on available datasets.

scholarly journals MALIGNANT MELANOMA AND PREGNANCY

2021 ◽  
Vol 68 (2) ◽  
pp. 143-146
Author(s):  
Delia Cudalbă ◽  
◽  
Nicolae Gică ◽  
Radu Botezatu ◽  
Corina Gică ◽  
...  
Keyword(s):  
Malignant Melanoma ◽  
Poor Prognosis ◽  
Skin Lesions ◽  
Fetal Medicine ◽  
Treatment Need ◽  
Pigmented Skin Lesions ◽  
Pregnant State ◽  
State Diagnosis ◽  
Patients With Poor Prognosis ◽  
In Pregnancy

Malignant melanoma is one of the most frequent cancers diagnosed during pregnancy. Any pigmented skin lesions that change the color should be examined by an experienced dermatologist and if suspected, should be biopsied. Recent studies showed that malignant melanoma in pregnancy has not a worse outcome compared with non-pregnant state. Diagnosis of melanoma does not require an early delivery excepted pregnant patients with poor prognosis that need more aggressive treatment. Diagnosis and treatment need to be established in specialized centers with a multidisciplinary team. Pregnancy monitoring should be performed by team consisting of an obstetrician, a neonatologist and a specialist in fetal medicine.

Malignant melanoma

2019 ◽  
Vol 12 (6) ◽  
pp. 297-303
Author(s):  
Charlotte Sidebotham
Keyword(s):  
Public Health ◽  
Malignant Melanoma ◽  
Skin Lesions ◽  
Health Concern ◽  
Public Health Concern ◽  
Favourable Prognosis ◽  
Younger Age ◽  
Pigmented Skin Lesions ◽  
Early Primary

Melanoma affects people at a younger age than other cancers, and can rapidly become fatal if not detected. Thus, pigmented skin lesions can cause concern in both doctors and patients. If discovered in the early primary stages, melanomas have a favourable prognosis. Over the last few decades, the incidence of melanoma has significantly increased, and is fast becoming an urgent public health concern. This article aims to shine a light on this dangerous adversary.

Prognosis after Surgical Excision of Canine Melanomas

1979 ◽  
Vol 16 (1) ◽  
pp. 32-40 ◽  
Author(s):  
D. E. Bostock
Keyword(s):  
Malignant Melanoma ◽  
Oral Cavity ◽  
Mitotic Index ◽  
Surgical Excision ◽  
Skin Lesions ◽  
Skin Tumours ◽  
Malignant Skin ◽  
Index 2 ◽  
Eventual Death

One hundred and thirty-four dogs from which melanomas had been excised were studied until death or for at least 2 years after surgery. Seven of 49 (14%) intraoral and lip tumours and 52 of 85 (61%) skin tumours were histologically benign; in spite of this, three of seven (43%) “benign” oral and four of 52 (8%) “benign” skin lesions led to the eventual death of the host. Thirty eight of 42 (90%) dogs with a histologically malignant melanoma of the lip or oral cavity died because of the tumour but only 15 of 33 (45%) with malignant skin melanomas died. Six of 59 (10%) dogs with a tumour of mitotic index 2 or less died from the tumour 2 years after surgery compared to 19 of 26 (73%) dogs having a tumour with a mitotic index of 3 or more.

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