A robust method for skin cancer diagnosis based on interval analysis

Zhang, Haohai; Wang, Zhijun; Liang, Liping; Sheykhahmad, Fatima Rashid

doi:10.1080/00051144.2020.1835108

Cited by 7 publications

(1 citation statement)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Through dermoscopy technology, RGB images of the skin are captured and later analyzed by experts. A computerized method consists of the following steps: preprocessing of dermoscopic images, segmentation of skin lesions, feature extraction, and finally classification [7]. Preprocessing is the step in which low-contrast images are enhanced and artifacts such as hair and noise can be removed through different dermoscopic image techniques [8].…”

Section: Introductionmentioning

confidence: 99%

Skin Lesion Segmentation and Classification Using Conventional and Deep Learning Based Framework

Bibi¹,

Khan²,

Javed³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Background: In medical image analysis, the diagnosis of skin lesions remains a challenging task. Skin lesion is a common type of skin cancer that exists worldwide. Dermoscopy is one of the latest technologies used for the diagnosis of skin cancer. Challenges: Many computerized methods have been introduced in the literature to classify skin cancers. However, challenges remain such as imbalanced datasets, low contrast lesions, and the extraction of irrelevant or redundant features. Proposed Work: In this study, a new technique is proposed based on the conventional and deep learning framework. The proposed framework consists of two major tasks: lesion segmentation and classification. In the lesion segmentation task, contrast is initially improved by the fusion of two filtering techniques and then performed a color transformation to color lesion area color discrimination. Subsequently, the best channel is selected and the lesion map is computed, which is further converted into a binary form using a thresholding function. In the lesion classification task, two pre-trained CNN models were modified and trained using transfer learning. Deep features were extracted from both models and fused using canonical correlation analysis. During the fusion process, a few redundant features were also added, lowering classification accuracy. A new technique called maximum entropy score-based selection (MESbS) is proposed as a solution to this issue. The features selected through this approach are fed into a cubic support vector machine (C-SVM) for the final classification. Results: The experimental process was conducted on two datasets: ISIC 2017 and HAM10000. The ISIC 2017 dataset was used for the lesion segmentation task, whereas the HAM10000 dataset was used for the classification task. The achieved accuracy

show abstract

Section: Introductionmentioning

confidence: 99%

Skin Lesion Segmentation and Classification Using Conventional and Deep Learning Based Framework

Bibi¹,

Khan²,

Javed³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

Comparative Analysis of Eight Direction Sobel Edge Detection Algorithm for Brain Tumor MRI Images

Gopalan²

2022

Procedia Computer Science

View full text Add to dashboard Cite

Skin cancer diagnosis (SCD) using Artificial Neural Network (ANN) and Improved Gray Wolf Optimization (IGWO)

Lai,

Kuang,

Wang

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Skin Cancer (SC) is one of the most dangerous types of cancer and if not treated in time, it can threaten the patient’s life. With early diagnosis of this disease, treatment methods can be used more effectively and the progression of the disease can be prevented. Machine Learning (ML) techniques can be utilized as a useful and efficient tool for SCD. So far, various methods for automatic SCD based on ML techniques have been presented; However, this research field still requires the application of optimal and efficient models to increase the accuracy of SCD. Therefore, in this article, a new method for SCD using a combination of optimization techniques and Artificial Neural Networks (ANNs) is presented. The proposed method includes four steps: pre-processing, segmentation, feature extraction, and classification. Image segmentation for identifying the lesion region is performed using a Kohonen neural network, where the identified region of interest (ROI) is enhanced using the Greedy Search Algorithm (GSA). The proposed method, uses a Convolutional Neural Network (CNN) for extracting features from ROIs. Also, to classify features, an ANN is used, and by the Improved Gray Wolf Optimization (IGWO) algorithm, the number of neurons and weight vector are adjusted. In this method, a probabilistic model is used to improve the convergence speed of the GWO algorithm. Based on the evaluation results, using the IGWO model to optimize the structure and weight vector of the ANN can be effective in increasing the diagnosis accuracy by at least 5%. The results of implementing the proposed method and comparing its performance with previous methods also show that this method can diagnose SC in the ISIC-2016 and ISIC-2017 databases with an average accuracy of 97.09 and 95.17%, respectively; which improves accuracy by at least 0.5% compared to other methods.

show abstract

A robust method for skin cancer diagnosis based on interval analysis

Cited by 7 publications

References 41 publications

Skin Lesion Segmentation and Classification Using Conventional and Deep Learning Based Framework

Skin Lesion Segmentation and Classification Using Conventional and Deep Learning Based Framework

Comparative Analysis of Eight Direction Sobel Edge Detection Algorithm for Brain Tumor MRI Images

Skin cancer diagnosis (SCD) using Artificial Neural Network (ANN) and Improved Gray Wolf Optimization (IGWO)

Contact Info

Product

Resources

About