scholarly journals Classification of Dermoscopic Image of Skin Cancer Using the GLCM Method and Multi-SVM Algorithm

Rekayasa ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 407-415
Author(s):  
Riyan Latifahul Hasanah ◽  
Dwiza Riana
Keyword(s):  
Skin Cancer ◽  
Correlation Energy ◽  
Texture Feature ◽  
Skin Lesions ◽  
Pigment Cells ◽  
Binary Images ◽  
Svm Algorithm ◽  
Dermoscopic Image ◽  
Processing Techniques

The development of abnormal skin pigment cells can cause a skin cancer called melanoma. Melanoma can be cured if diagnosed and treated in its early stages. Various studies using various technologies have been developed to conduct early detection of melanoma. This research was conducted to diagnose melanoma skin cancer with digital image processing techniques on the dermoscopic image of skin cancer. The diagnosis is made by classifying dermoscopic images based on the types of Common Nevus, Atypical Nevus or Melanoma. Pre-processing is done by changing the RGB image to grayscale (grayscaling), smoothing image using median filtering, and image segmentation based on binary images of skin lesions. The value of Contrast, Correlation, Energy and Homogeneity obtained from the texture feature extraction of the GLCM method is used in the next step, which is the classification process with the Multi-SVM algorithm. The proposed research method shows high accuracy results in diagnosing skin cancer

scholarly journals Design of a tool for the classification of skin cancer images using Deep Neural Networks (DNN)

2021 ◽  
pp. 65-80
Author(s):  
Diana Paola Merchán Vargas ◽  
Helis Navarro Báez ◽  
Jaime Guillermo Barrero Pérez ◽  
Jeyson Arley Castillo Bohórquez
Keyword(s):  
Skin Cancer ◽  
Embedded System ◽  
Image Classification ◽  
Life Quality ◽  
Skin Lesions ◽  
Raspberry Pi ◽  
World Population ◽  
Advanced Stages ◽  
Reliable Solution

Skin cancer is one of the most common diseases in the world population. Usually, the diagnosis requires the acquisition of dermatoscopic images. Both biopsy and histopathology have been used in advanced stages. Its early detection is very important to increase patient life quality and life expectancy. In Colombia, the lack of qualified professionals and medical instruments difficulties this task. The automatic classification is a huge challenge, due to ample variety and morphology in skin lesions. Nowadays, Deep Learning reaches elevated accuracy levels in image classification tasks and is set to become a reliable solution for medical image classification. In this research, used these DNN advantages to build a convolutional neural network (CNN) trained with open source databases to the classification of skin lesions benign and malignant. After the training process, we develop an embedded system with raspberry Pi 3 B+ with a generic camera and implemented the CNN described in Python coded-based. For the benign and malignant classification, the prototype reached an accuracy level of 91.06% in the F1 score and a recall of 91.98%.

scholarly journals Computer-Aided Diagnosis of Micro-Malignant Melanoma Lesions Applying Support Vector Machines

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Joanna Jaworek-Korjakowska
Keyword(s):  
Skin Cancer ◽  
Malignant Melanoma ◽  
Early Stage ◽  
Skin Lesions ◽  
Support Vector ◽  
Svm Classifier ◽  
Stage Of Development ◽  
Vector Machines ◽  
Aided Diagnosis

Background. One of the fatal disorders causing death is malignant melanoma, the deadliest form of skin cancer. The aim of the modern dermatology is the early detection of skin cancer, which usually results in reducing the mortality rate and less extensive treatment. This paper presents a study on classification of melanoma in the early stage of development using SVMs as a useful technique for data classification.Method. In this paper an automatic algorithm for the classification of melanomas in their early stage, with a diameter under 5 mm, has been presented. The system contains the following steps: image enhancement, lesion segmentation, feature calculation and selection, and classification stage using SVMs.Results. The algorithm has been tested on 200 images including 70 melanomas and 130 benign lesions. The SVM classifier achieved sensitivity of 90% and specificity of 96%. The results indicate that the proposed approach captured most of the malignant cases and could provide reliable information for effective skin mole examination.Conclusions. Micro-melanomas due to the small size and low advancement of development create enormous difficulties during the diagnosis even for experts. The use of advanced equipment and sophisticated computer systems can help in the early diagnosis of skin lesions.

scholarly journals Skin Cancer Diseases Classification using Deep Convolutional Neural Network with Transfer Learning Model

2021 ◽  
Vol 2128 (1) ◽  
pp. 012013
Author(s):  
Laila Moataz ◽  
Gouda I. Salama ◽  
Mohamed H. Abd Elazeem
Keyword(s):  
Neural Network ◽  
Skin Cancer ◽  
Classification Accuracy ◽  
Skin Lesions ◽  
Fine Tuning ◽  
Deep Convolutional Neural Networks ◽  
Proposed Model ◽  
Prior Models ◽  
Cancer Diseases

Abstract Skin cancer is becoming increasingly common. Fortunately, early discovery can greatly improve the odds of a patient being healed. Many Artificial Intelligence based approaches to classify skin lesions have recently been proposed. but these approaches suffer from limited classification accuracy. Deep convolutional neural networks show potential for better classification of cancer lesions. This paper presents a fine-tuning on Xception pretrained model for classification of skin lesions by adding a group of layers after the basic ones of the Xception model and all model weights are set to be trained. The model is fine-tuned over HAM10,000 dataset seven classes by augmentation approach to mitigate the data imbalance effect and conducted a comparative study with the most up to date approaches. In comparison to prior models, the results indicate that the proposed model is both efficient and reliable.

scholarly journals Automatic Image Characterization of Psoriasis Lesions

Mathematics ◽  
2021 ◽  
Vol 9 (22) ◽  
pp. 2974
Author(s):  
Javier Martínez-Torres ◽  
Alicia Silva Piñeiro ◽  
Álvaro Alesanco ◽  
Ignacio Pérez-Rey ◽  
José García
Keyword(s):  
Software Tool ◽  
Skin Lesions ◽  
Contour Detection ◽  
Areal Measurement ◽  
Chronic Skin ◽  
Nipple Detection ◽  
Processing Techniques ◽  
Chronic Skin Disease

Psoriasis is a chronic skin disease that affects 125 million people worldwide and, particularly, 2% of the Spanish population, characterized by the appearance of skin lesions due to a growth of the epidermis that is seven times larger than usual. Its diagnosis and monitoring are based on the use of methodologies for measuring the severity and extent of these spots, and this includes a large subjective component. For this reason, this paper presents an automatic method for characterizing psoriasis images that is divided into four parts: image preparation or pre-processing, feature extraction, classification of the lesions, and the obtaining of parameters. The methodology proposed in this work covers different digital-image processing techniques, namely, marker-based image delimitation, hair removal, nipple detection, lesion contour detection, areal-measurement-based lesion classification, as well as lesion characterization by means of red and white intensity. The results obtained were also endorsed by a professional dermatologist. This methodology provides professionals with a common software tool for monitoring the different existing typologies, which proved satisfactory in the cases analyzed for a set of 20 images corresponding to different types of lesions.

scholarly journals Non-Invasive Skin Cancer Diagnosis Using Hyperspectral Imaging for In-Situ Clinical Support

2020 ◽  
Vol 9 (6) ◽  
pp. 1662 ◽  
Author(s):  
Raquel Leon ◽  
Beatriz Martinez-Vega ◽  
Himar Fabelo ◽  
Samuel Ortega ◽  
Veronica Melian ◽  
...  
Keyword(s):  
Skin Cancer ◽  
Cancer Diagnosis ◽  
Skin Lesions ◽  
Morphological Processing ◽  
Automatic Identification ◽  
Routine Practice ◽  
Non Invasive ◽  
Spectral Bands ◽  
Clinical Support

Skin cancer is one of the most common forms of cancer worldwide and its early detection its key to achieve an effective treatment of the lesion. Commonly, skin cancer diagnosis is based on dermatologist expertise and pathological assessment of biopsies. Although there are diagnosis aid systems based on morphological processing algorithms using conventional imaging, currently, these systems have reached their limit and are not able to outperform dermatologists. In this sense, hyperspectral (HS) imaging (HSI) arises as a new non-invasive technology able to facilitate the detection and classification of pigmented skin lesions (PSLs), employing the spectral properties of the captured sample within and beyond the human eye capabilities. This paper presents a research carried out to develop a dermatological acquisition system based on HSI, employing 125 spectral bands captured between 450 and 950 nm. A database composed of 76 HS PSL images from 61 patients was obtained and labeled and classified into benign and malignant classes. A processing framework is proposed for the automatic identification and classification of the PSL based on a combination of unsupervised and supervised algorithms. Sensitivity and specificity results of 87.5% and 100%, respectively, were obtained in the discrimination of malignant and benign PSLs. This preliminary study demonstrates, as a proof-of-concept, the potential of HSI technology to assist dermatologists in the discrimination of benign and malignant PSLs during clinical routine practice using a real-time and non-invasive hand-held device.

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