New Auxiliary Function with Properties in Nonsmooth Global Optimization for Melanoma Skin Cancer Segmentation

Abdulhamid, Idris A. Masoud; Şahiner, Ahmet; Rahebi, Javad

doi:10.1155/2020/5345923

Cited by 21 publications

(12 citation statements)

References 42 publications

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“…If it is expressed by percentage, the proximity to %100 shows the accuracy of the proposed method in the segmentation and extraction of MS areas. TP, TN, FP, and FN have different meanings and count the true or false pixels pertaining to MS tissue or healthy tissue [ 25 – 27 ]. Table 1 shows the meanings of these indices.…”

Section: Implementation and Analysismentioning

confidence: 99%

[Retracted] Diagnosis of Multiple Sclerosis Disease in Brain Magnetic Resonance Imaging Based on the Harris Hawks Optimization Algorithm

Iswisi

Karan

Rahebi

2021

BioMed Research International

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The damaged areas of brain tissues can be extracted by using segmentation methods, most of which are based on the integration of machine learning and data mining techniques. An important segmentation method is to utilize clustering techniques, especially the fuzzy C-means (FCM) clustering technique, which is sufficiently accurate and not overly sensitive to imaging noise. Therefore, the FCM technique is appropriate for multiple sclerosis diagnosis, although the optimal selection of cluster centers can affect segmentation. They are difficult to select because this is an NP-hard problem. In this study, the Harris Hawks optimization (HHO) algorithm was used for the optimal selection of cluster centers in segmentation and FCM algorithms. The HHO is more accurate than other conventional algorithms such as the genetic algorithm and particle swarm optimization. In the proposed method, every membership matrix is assumed as a hawk or an HHO member. The next step is to generate a population of hawks or membership matrices, the most optimal of which is selected to find the optimal cluster centers to decrease the multiple sclerosis clustering error. According to the tests conducted on a number of brain MRIs, the proposed method outperformed the FCM clustering and other techniques such as the k -NN algorithm, support vector machine, and hybrid data mining methods in accuracy.

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Section: Implementation and Analysismentioning

confidence: 99%

[Retracted] Diagnosis of Multiple Sclerosis Disease in Brain Magnetic Resonance Imaging Based on the Harris Hawks Optimization Algorithm

Iswisi

Karan

Rahebi

2021

BioMed Research International

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“…Coronaviruses are known to cause earnest fatal lung diseases. Coronaviruses are the primary cause of many colds diseases, but three of them are very deadly (Ahsan et al 2020 ; Abdulhamid et al 2020 ). In the first phase of the spread of these diseases, transmission between humans and animals was ordinary, but over time, it has become a human-to-human infection.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Feature selection for diagnose coronavirus (COVID-19) disease by neural network and Caledonian crow learning algorithm

alsaeedi

Kurnaz

2022

Appl Nanosci

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In this study, feature selection methods based on the new Caledonian crow learning algorithm has been introduced. In the proposed algorithms, in the first stage, the best features related to COVID-19 disease are selected by the crow learning algorithm. Coronavirus (COVIDE-19) disease using as training input to the artificial neural network. Experiments on the COVID-19 disease dataset in a Brazilian hospital show that the crow learning algorithm reduces the feature selection objective function by iteration. Decreasing the feature selection function is due to reducing the error of classifying infected people as healthy and reducing the number of features. The experimental results show that the accuracy, sensitivity, precision, and F1 of the proposed method for COVID-19 patients diagnosing are 94.31%, 94.15%, 94.38%, and 94.27%, respectively. The proposed method for identifying COVID-19 patients is more accurate than ANN, CNN, CNNLSTM, CNNRNN, LSTM, and RNN methods.

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“…A skin lesion segmentation technique was designed that established adaptive thresholding with the normalization of color networks for dermoscopic images [ 67 ]. The researchers designed an algorithm that solves the problem of global optimization as it established an auxiliary function that was smoothed by utilizing Bezier curves and constructed using a local minimizer [ 68 ]. Active contour fusion segmentation is utilized and the main focus is to segment low-contrast dermoscopic samples [ 50 ].…”

Section: Skin Cancer Recognition and Classification Systemmentioning

confidence: 99%

“… Segmentation Results Concerning the Accuracy [ 50 , 60 , 61 , 62 , 63 , 66 , 68 , 72 , 73 , 74 , 75 , 77 , 80 , 85 ]. …”

Section: Figurementioning

confidence: 99%

Skin Lesion Analysis and Cancer Detection Based on Machine/Deep Learning Techniques: A Comprehensive Survey

Zafar

Sharif

et al. 2023

Life

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The skin is the human body’s largest organ and its cancer is considered among the most dangerous kinds of cancer. Various pathological variations in the human body can cause abnormal cell growth due to genetic disorders. These changes in human skin cells are very dangerous. Skin cancer slowly develops over further parts of the body and because of the high mortality rate of skin cancer, early diagnosis is essential. The visual checkup and the manual examination of the skin lesions are very tricky for the determination of skin cancer. Considering these concerns, numerous early recognition approaches have been proposed for skin cancer. With the fast progression in computer-aided diagnosis systems, a variety of deep learning, machine learning, and computer vision approaches were merged for the determination of medical samples and uncommon skin lesion samples. This research provides an extensive literature review of the methodologies, techniques, and approaches applied for the examination of skin lesions to date. This survey includes preprocessing, segmentation, feature extraction, selection, and classification approaches for skin cancer recognition. The results of these approaches are very impressive but still, some challenges occur in the analysis of skin lesions because of complex and rare features. Hence, the main objective is to examine the existing techniques utilized in the discovery of skin cancer by finding the obstacle that helps researchers contribute to future research.

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New Auxiliary Function with Properties in Nonsmooth Global Optimization for Melanoma Skin Cancer Segmentation

Cited by 21 publications

References 42 publications

[Retracted] Diagnosis of Multiple Sclerosis Disease in Brain Magnetic Resonance Imaging Based on the Harris Hawks Optimization Algorithm

[Retracted] Diagnosis of Multiple Sclerosis Disease in Brain Magnetic Resonance Imaging Based on the Harris Hawks Optimization Algorithm

RETRACTED ARTICLE: Feature selection for diagnose coronavirus (COVID-19) disease by neural network and Caledonian crow learning algorithm

Skin Lesion Analysis and Cancer Detection Based on Machine/Deep Learning Techniques: A Comprehensive Survey

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