scholarly journals COMPUTER VISION FOR SKIN CANCER DETECTION AND DIAGNOSIS

2020 ◽  
Author(s):  
Farah Shahata ◽  
Kamalpreet Kaur ◽  
Jinan Fiaidhi
Keyword(s):  
Computer Vision ◽  
Skin Cancer ◽  
Web Service ◽  
Cancer Detection ◽  
Skin Imaging ◽  
Proposed Model ◽  
Diagnosis Accuracy ◽  
Detection And Diagnosis ◽  
Skin Cancer Detection ◽  
Multi Classification

<b>— Skin-cancer is the most common type of cancer among all other types of cancers spreading both developed and developing countries. In this paper, a web service is developed in order to help physicians as well as individuals to upload an image and diagnose the particular types of lesion. Computer vision is used to analyse lesions on images by providing computers with somewhat similarity as humans do. For this a Convolution Neural Network (CNN) is used with multi classification on International Skin Imaging Collaboration (ISIC) 2018 dataset with HAM10000 images. This dataset is a meta-data which has various types of images with seven different labels. At first, the model is trained with a larger training set and saved in a zip folder. Secondly, a web service is created where users or a doctor can upload an image for classification. Thirdly, the images uploaded are pre-processed as there is noise, hairs on image. Techniques like resizing, normalisation, thresholding, black-hat filtering and inpainting are used for this purpose. After this, the saved model is called to define whether the uploaded image is benign or malignant. The experimental results reveal that the proposed model is superior in terms of detection and diagnosis accuracy as compared to modern methods.</b>

scholarly journals COMPUTER VISION FOR SKIN CANCER DETECTION AND DIAGNOSIS

2020 ◽  
Author(s):  
Farah Shahata ◽  
Kamalpreet Kaur ◽  
Jinan Fiaidhi
Keyword(s):  
Computer Vision ◽  
Skin Cancer ◽  
Web Service ◽  
Cancer Detection ◽  
Skin Imaging ◽  
Proposed Model ◽  
Diagnosis Accuracy ◽  
Detection And Diagnosis ◽  
Skin Cancer Detection ◽  
Multi Classification

<b>— Skin-cancer is the most common type of cancer among all other types of cancers spreading both developed and developing countries. In this paper, a web service is developed in order to help physicians as well as individuals to upload an image and diagnose the particular types of lesion. Computer vision is used to analyse lesions on images by providing computers with somewhat similarity as humans do. For this a Convolution Neural Network (CNN) is used with multi classification on International Skin Imaging Collaboration (ISIC) 2018 dataset with HAM10000 images. This dataset is a meta-data which has various types of images with seven different labels. At first, the model is trained with a larger training set and saved in a zip folder. Secondly, a web service is created where users or a doctor can upload an image for classification. Thirdly, the images uploaded are pre-processed as there is noise, hairs on image. Techniques like resizing, normalisation, thresholding, black-hat filtering and inpainting are used for this purpose. After this, the saved model is called to define whether the uploaded image is benign or malignant. The experimental results reveal that the proposed model is superior in terms of detection and diagnosis accuracy as compared to modern methods.</b>

COMPUTER VISION FOR SKIN CANCER DETECTION AND DIAGNOSIS

2020 ◽  
Author(s):  
Kamalpreet Kaur ◽  
Lakshman Boddu ◽  
Jinan Fiaidhi
Keyword(s):  
Computer Vision ◽  
Skin Cancer ◽  
Web Service ◽  
Cancer Detection ◽  
Skin Imaging ◽  
Proposed Model ◽  
Diagnosis Accuracy ◽  
Detection And Diagnosis ◽  
Skin Cancer Detection ◽  
Multi Classification

<b>Skin-cancer is the most common type of cancer among all other types of cancers spreading both developed and developing countries. In this paper, a web service is developed in order to help physicians as well as individuals to upload an image and diagnose the particular types of lesions. Computer vision is used to analyse lesions on images by providing computers with somewhat similarity as humans do. For this a Convolution Neural Network (CNN) is used with multi classification on International Skin Imaging Collaboration (ISIC) 2018 dataset with HAM10000 images. This dataset is a meta-data which has various types of images with seven different labels. At first, the model is trained with a larger training set and saved in a zip folder. Secondly, a web service is created where users or a doctor can upload an image for classification. Thirdly, the images uploaded are pre-processed as there is noise, hairs on image. Techniques like resizing, normalisation, thresholding, black-hat filtering and inpainting are used for this purpose. After this, the saved model is called to define whether the uploaded image is benign or malignant. The experimental results reveal that the proposed model is superior in terms of detection and diagnosis accuracy as compared to modern methods.</b>

scholarly journals COMPUTER VISION FOR SKIN CANCER DETECTION AND DIAGNOSIS

2020 ◽  
Author(s):  
Kamalpreet Kaur ◽  
Lakshman Boddu ◽  
Jinan Fiaidhi
Keyword(s):  
Computer Vision ◽  
Skin Cancer ◽  
Web Service ◽  
Cancer Detection ◽  
Skin Imaging ◽  
Proposed Model ◽  
Diagnosis Accuracy ◽  
Detection And Diagnosis ◽  
Skin Cancer Detection ◽  
Multi Classification

<b>Skin-cancer is the most common type of cancer among all other types of cancers spreading both developed and developing countries. In this paper, a web service is developed in order to help physicians as well as individuals to upload an image and diagnose the particular types of lesions. Computer vision is used to analyse lesions on images by providing computers with somewhat similarity as humans do. For this a Convolution Neural Network (CNN) is used with multi classification on International Skin Imaging Collaboration (ISIC) 2018 dataset with HAM10000 images. This dataset is a meta-data which has various types of images with seven different labels. At first, the model is trained with a larger training set and saved in a zip folder. Secondly, a web service is created where users or a doctor can upload an image for classification. Thirdly, the images uploaded are pre-processed as there is noise, hairs on image. Techniques like resizing, normalisation, thresholding, black-hat filtering and inpainting are used for this purpose. After this, the saved model is called to define whether the uploaded image is benign or malignant. The experimental results reveal that the proposed model is superior in terms of detection and diagnosis accuracy as compared to modern methods.</b>

scholarly journals Skin Lesion Classification Based on Deep Convolutional Neural Networks Architectures

2021 ◽  
Vol 2 (01) ◽  
pp. 41-51
Author(s):  
Jwan Saeed ◽  
Subhi Zeebaree
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Skin Cancer ◽  
Cancer Detection ◽  
Deep Convolutional Neural Networks ◽  
Color Structure ◽  
Malignant Skin ◽  
Dermatological Disorders ◽  
Detection And Diagnosis ◽  
Skin Cancer Detection

Skin cancer is among the primary cancer types that manifest due to various dermatological disorders, which may be further classified into several types based on morphological features, color, structure, and texture. The mortality rate of patients who have skin cancer is contingent on preliminary and rapid detection and diagnosis of malignant skin cancer cells. Limitations in current dermoscopic images, including shadow, artifact, and noise, affect image quality, which may hamper detection effort. Attempts to overcome these challenges have been made by analyzing the images using deep learning neural networks to perform skin cancer detection. In this paper, the authors review the state-of-the-art in authoritative deep learning concepts pertinent to skin cancer detection and classification.

Skin Cancer Detection and Severity Prediction Using Computer Vision and Deep Learning

2021 ◽  
pp. 295-304
Author(s):  
Sangeeta Parshionikar ◽  
Renjit Koshy ◽  
Aman Sheikh ◽  
Gauravi Phansalkar
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Skin Cancer ◽  
Cancer Detection ◽  
Severity Prediction ◽  
Skin Cancer Detection

Raman Spectroscopy Techniques for Skin Cancer Detection and Diagnosis

2020 ◽  
pp. 359-393
Author(s):  
Ivan A. Bratchenko ◽  
Dmitry N. Artemyev ◽  
Yulia A. Khristoforova ◽  
Lyudmila A. Bratchenko ◽  
Oleg O. Myakinin ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Skin Cancer ◽  
Cancer Detection ◽  
Detection And Diagnosis ◽  
Skin Cancer Detection

scholarly journals Automated Non-invasive Skin Cancer Detection using Dermoscopic Images

2021 ◽  
Vol 40 ◽  
pp. 03044
Author(s):  
Shruti Kale ◽  
Reema Kharat ◽  
Sagarika Kalyankar ◽  
Sangita Chaudhari ◽  
Apurva Shinde
Keyword(s):  
Skin Cancer ◽  
Early Detection ◽  
Cancer Detection ◽  
Medical Practitioners ◽  
Non Invasive ◽  
Skin Cancers ◽  
Proposed Model ◽  
Biopsy Methods ◽  
Treatment Procedures ◽  
Skin Cancer Detection

Skin Cancer is resulting from the growth of the harmful tumour of the melanocytes the rates are rising to another level. The medical business is advancing with the innovation of recent technologies; newer tending technology and treatment procedures are being developed. The early detection of skin cancer can help the chance of increase in its growth in other parts of body. In recent years, medical practitioners tend to use non invasive Computer aided system to detect the skin cancers in early phase of its spreading instead of relying on traditional skin biopsy methods. Convolution neural network model is proposed and used for early detection of the cancer, and it type. The proposed model could classify the dermoscopic images into correct type with accuracy 91.2%.

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