Detection of skin cancer with adaptive fuzzy classifier using improved whale optimization

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Nagayalanka Durgarao ◽  
Ghanta Sudhavani
Keyword(s):  
Skin Cancer ◽  
Type I ◽  
Fuzzy Classifier ◽  
Detection Scheme ◽  
Clustering Model ◽  
Proposed Model ◽  
Whale Optimization ◽  
Local Vector ◽  
Skin Cancer Detection ◽  
Local Vector Pattern

AbstractSkin cancer is considered as a well-known type of cancer globally, and its occurrence has been found to be raised in current days. Researchers state that the disease requires early prediction so that the identification of precise signs will make it simple for the dermatologists and clinicians. This disorder has been established to be unpredictable. Hence, this paper intends to develop an efficient skin cancer detection scheme, which classifies the nature of cancer, whether it is normal, benign or malignant. Accordingly, the skin image which is given as input is segmented using k-means clustering model and the features are extracted from segmented image using Local Vector Pattern (LVP). Moreover, the extracted features are subjected to fuzzy classifier for recognizing the cancer. In addition, the limits of membership functions are optimally selected by improved Whale Optimization Algorithm (WOA). Thus, the proposed scheme is termed as Improved Selection of Encircling and Spiral updating position of WO-based Fuzzy Classifier (ISESW-FC). From the optimized output, the type of skin cancer image can be determined, whether it is normal, benign or malignant. The performance of proposed model is compared over other conventional methods, and its efficiency is proved by means of Type I and Type II measures.

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>

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%.

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 Building a Fuzzy Classifier Based on Whale Optimization Algorithm to Detect Network Intrusions

Symmetry ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1211
Author(s):  
Nikolay Koryshev ◽  
Ilya Hodashinsky ◽  
Alexander Shelupanov
Keyword(s):  
Intrusion Detection ◽  
Optimization Algorithm ◽  
Type I Error ◽  
Whale Optimization Algorithm ◽  
Type I ◽  
Features Selection ◽  
Fuzzy Classifier ◽  
Detection Systems ◽  
Urgent Task ◽  
Whale Optimization

The quantity of network attacks and the harm from them is constantly increasing, so the detection of these attacks is an urgent task in the information security field. In this paper, we investigate an approach to building intrusion detection systems using a classifier based on fuzzy rules. The process of creating a fuzzy classifier based on a given set of input and output data can be presented as a solution to the problems of clustering, informative features selection, and the parameters of the rule antecedents optimization. To solve these problems, the whale optimization algorithm is used. The performance of algorithms for constructing a fuzzy classifier based on this metaheuristic is estimated using the KDD Cup 1999 intrusion detection dataset. On average, the resulting classifiers have a type I error of 0.92% and a type II error of 1.07%. The obtained results are also compared with the results of other classifiers. The comparison shows the competitiveness of the proposed method.

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>

Automated Skin Cancer Detection Web-Based Application and Review of Some Image Classification Algorithm

2020 ◽  
Author(s):  
Siddhesh Bhojane ◽  
Krishna Shrestha ◽  
Sanghmitra Bharadwaj ◽  
Ritul Yadav ◽  
Fenil Ribinwala ◽  
...  
Keyword(s):  
Skin Cancer ◽  
Image Classification ◽  
Cancer Detection ◽  
Classification Algorithm ◽  
Web Based ◽  
Skin Cancer Detection
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