Application of multi-classification method of skin cancer based on dermoscopic image

2021 ◽  
Author(s):  
Qian Chen ◽  
Min Li ◽  
Cheng Chen ◽  
Chen Chen ◽  
Xiaoyi Lv
Keyword(s):  
Skin Cancer ◽  
Classification Method ◽  
Dermoscopic Image ◽  
Multi Classification

scholarly journals Skin lesion segmentation method for dermoscopic images with convolutional neural networks and semantic segmentation

2021 ◽  
Vol 45 (1) ◽  
pp. 122-129
Author(s):  
Dang N.H. Thanh ◽  
Nguyen Hoang Hai ◽  
Le Minh Hieu ◽  
Prayag Tiwari ◽  
V.B. Surya Prasath
Keyword(s):  
Skin Cancer ◽  
Skin Lesion ◽  
Semantic Segmentation ◽  
Segmentation Method ◽  
Lesion Segmentation ◽  
Computing Systems ◽  
Segmentation Methods ◽  
Dermoscopic Image ◽  
Better Than

Melanoma skin cancer is one of the most dangerous forms of skin cancer because it grows fast and causes most of the skin cancer deaths. Hence, early detection is a very important task to treat melanoma. In this article, we propose a skin lesion segmentation method for dermoscopic images based on the U-Net architecture with VGG-16 encoder and the semantic segmentation. Base on the segmented skin lesion, diagnostic imaging systems can evaluate skin lesion features to classify them. The proposed method requires fewer resources for training, and it is suitable for computing systems without powerful GPUs, but the training accuracy is still high enough (above 95 %). In the experiments, we train the model on the ISIC dataset – a common dermoscopic image dataset. To assess the performance of the proposed skin lesion segmentation method, we evaluate the Sorensen-Dice and the Jaccard scores and compare to other deep learning-based skin lesion segmentation methods. Experimental results showed that skin lesion segmentation quality of the proposed method are better than ones of the compared methods.

Dermoscopic Image Classification Method Based on FL-ResNet50

2020 ◽  
Vol 57 (18) ◽  
pp. 181022
Author(s):  
罗清 Luo Qing ◽  
周维 Zhou Wei ◽  
马梓钧 Ma Zijun ◽  
许海霞 Xu Haixia
Keyword(s):  
Image Classification ◽  
Classification Method ◽  
Dermoscopic Image

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 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 A Multi-Classification Method of Improved SVM-based Information Fusion for Traffic Parameters Forecasting

2016 ◽  
Vol 28 (2) ◽  
pp. 117-124 ◽  
Author(s):  
Hongzhuan Zhao ◽  
Dihua Sun ◽  
Min Zhao ◽  
Senlin Cheng
Keyword(s):  
Support Vector Machine ◽  
Information Fusion ◽  
Binary Tree ◽  
Classification Method ◽  
Traffic Information ◽  
Support Vector ◽  
Cyber Physical System ◽  
Svm Classification ◽  
Forecasting Performance ◽  
Multi Classification

With the enrichment of perception methods, modern transportation system has many physical objects whose states are influenced by many information factors so that it is a typical Cyber-Physical System (CPS). Thus, the traffic information is generally multi-sourced, heterogeneous and hierarchical. Existing research results show that the multisourced traffic information through accurate classification in the process of information fusion can achieve better parameters forecasting performance. For solving the problem of traffic information accurate classification, via analysing the characteristics of the multi-sourced traffic information and using redefined binary tree to overcome the shortcomings of the original Support Vector Machine (SVM) classification in information fusion, a multi-classification method using improved SVM in information fusion for traffic parameters forecasting is proposed. The experiment was conducted to examine the performance of the proposed scheme, and the results reveal that the method can get more accurate and practical outcomes.

scholarly journals Potential Detection Application of Nodular Melanoma on Melanocytic Nevi Image Based on Android

2019 ◽  
Vol 8 (2) ◽  
pp. 6285-6290
Keyword(s):  
Skin Cancer ◽  
Decision Tree ◽  
Image Data ◽  
The Body ◽  
Classification Method ◽  
Rare Type ◽  
Nodular Melanoma ◽  
Melanocytic Nevi ◽  
Physical Form ◽  
Asymmetrical Shape

Nodular melanoma is a deadly rare type of skin cancer. Nodular Melanoma has characteristics asymmetrical shape, border irregularity, nonhomogeneous or has several color variations and the diameter is more than 6 millimeters. Nodular melanoma has a physical form similar to melanocytic nevi, therefore nodular melanoma can be detected from melanocytic nevi spread throughout the body. This research aims to detect nodular melanoma through melanocytic nevi by utilizing the android system in order to ease the user by using camera smartphone in detecting cancer. This application uses image processing and feature extraction of the ABCD method to process images with decision tree c4.5 classification method to detect potential of nodular melanoma diagnosis from melanocytic nevi image. The ABCD method is a medical method used to detect the possibility of skin cancer using 4 parameters including asymmetrical shape, border irregularity, color and diameter. Decision tree c4.5 is classification method that using entropy and gain to make rules of decision tree. The image data test is obtained from the results of the android-based smartphone camera shooting and from medical record. Output of this application is a diagnosis condition of melanocytic nevi is healthy or nodular melanoma potentially. The accuracy of this application is 97.5%

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