Skin Melanoma Segmentation Using Neural Networks Optimized by Quantum Invasive Weed Optimization Algorithm

Razmjooy, Navid; Razmjooy, Saeid

doi:10.1007/978-3-030-56689-0_12

Cited by 23 publications

(13 citation statements)

References 20 publications

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“…First, the problem's image or data are detected and put into the algorithm. e output layer's hidden weights would subsequently manifest themselves in a variety of ways [55][56][57][58][59]. A classification or recognition technique is used when the output has many numerical components, such as a binary number or index (e.g., image classification, normal � 1, and abnormal � 2).…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Presentation of a Segmentation Method for a Diabetic Retinopathy Patient’s Fundus Region Detection Using a Convolutional Neural Network

Valizadeh

Ghoushchi

Ranjbarzadeh

et al. 2021

Computational Intelligence and Neuroscience

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Diabetic retinopathy is characteristic of a local distribution that involves early-stage risk factors and can forecast the evolution of the illness or morphological lesions related to the abnormality of retinal blood flows. Regional variations in retinal blood flow and modulation of retinal capillary width in the macular area and the retinal environment are also linked to the course of diabetic retinopathy. Despite the fact that diabetic retinopathy is frequent nowadays, it is hard to avoid. An ophthalmologist generally determines the seriousness of the retinopathy of the eye by directly examining color photos and evaluating them by visually inspecting the fundus. It is an expensive process because of the vast number of diabetic patients around the globe. We used the IDRiD data set that contains both typical diabetic retinopathic lesions and normal retinal structures. We provided a CNN architecture for the detection of the target region of 80 patients’ fundus imagery. Results demonstrate that the approach described here can nearly detect 83.84% of target locations. This result can potentially be utilized to monitor and regulate patients.

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Section: Convolutional Neural Networkmentioning

confidence: 99%

Presentation of a Segmentation Method for a Diabetic Retinopathy Patient’s Fundus Region Detection Using a Convolutional Neural Network

Valizadeh

Ghoushchi

Ranjbarzadeh

et al. 2021

Computational Intelligence and Neuroscience

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“…Among data conversion methods, data normalization has high efficiency. Normalization has different meanings in statistics, the simplest use of which is to normalize data or normalize variables, and is a method that puts data in the same domain when they are not [ 11 ]. In other words, a data miner may encounter situations where the properties of the data include values that are in different ranges or domains.…”

Section: Database Preprocessingmentioning

confidence: 99%

COVID-19 Diagnosis Using Capsule Network and Fuzzy $C$ -Means and Mayfly Optimization Algorithm

Farki

Salekshahrezaee

Tofigh

et al. 2021

BioMed Research International

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The COVID-19 epidemic is spreading day by day. Early diagnosis of this disease is essential to provide effective preventive and therapeutic measures. This process can be used by a computer-aided methodology to improve accuracy. In this study, a new and optimal method has been utilized for the diagnosis of COVID-19. Here, a method based on fuzzy C -ordered means (FCOM) along with an improved version of the enhanced capsule network (ECN) has been proposed for this purpose. The proposed ECN method is improved based on mayfly optimization (MFO) algorithm. The suggested technique is then implemented on the chest X-ray COVID-19 images from publicly available datasets. Simulation results are assessed by considering a comparison with some state-of-the-art methods, including FOMPA, MID, and 4S-DT. The results show that the proposed method with 97.08% accuracy and 97.29% precision provides the highest accuracy and reliability compared with the other studied methods. Moreover, the results show that the proposed method with a 97.1% sensitivity rate has the highest ratio. And finally, the proposed method with a 97.47% F 1 -score rate gives the uppermost value compared to the others.

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“…Based on the American Cancer Society, 6 in 2022 about 99 780 new melanomas are detected (42 600 in women and about 57 180 in men) and about 7650 people have been predicted to die of melanoma (2570 women and about 5080 men) 7 . One of the most threatening types of skin cancer is melan that appears on all parts of the skin or around moles 8 . Moreover, this type of cancer can be appeared in the eyes and hardly ever in the body, like the nose or throat.…”

Section: Introductionmentioning

confidence: 99%

“…7 One of the most threatening types of skin cancer is melan that appears on all parts of the skin or around moles. 8 Moreover, this type of cancer can be appeared in the eyes and hardly ever in the body, like the nose or throat. Melanoma expands due to the disorderly growth of melanocytes and therefore is often appeared in brown or black, even though it can appear in different colors like white or red.…”

Section: Introductionmentioning

confidence: 99%

“…Melanoma expands due to the disorderly growth of melanocytes and therefore is often appeared in brown or black, even though it can appear in different colors like white or red. 8 This type of cancer can metastasize quickly and influence relatively all body parts. It can be deadly when it grows excessively, however, is often effectively cured when detected soon.…”

Section: Introductionmentioning

confidence: 99%

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An optimal diagnosis system for melanoma dermoscopy images based on enhanced design of horse herd optimizer

Ding

Razmjooy

2023

Int J Imaging Syst Tech

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Skin cells grow in an orderly and controlled manner, in which old cells are pushed to the skin surface by healthy new cells, wherein they finally perish.However, when several cells damage DNA, new ones may start growing disorderly and can finally create cancer cells mass. Different types of skin cancer have been observed by experts. Although, melanoma, as one of the rarely happened cancers, is the most threatening type. Therefore, early detection of this type of cancer can be so useful for avoiding melanoma dangers and even helps to treat this type of cancer. The present paper proposes a new hierarchical procedure for the optimum diagnosis of melanoma cancer from dermoscopy images. Here, after image preprocessing, image segmentation is applied for the segmentation of the ROI. Then, the selection of features from the segmented images is performed and injected to a radial basis function-based classifier to provide the final diagnosis of cancer. To deliver efficient results, the feature selection and the classifier have been optimized by a new design of Horse herd optimization algorithm (HHOA). The method is then implemented on the Society for Imaging Informatics in Medicine-The International Skin Imaging Collaboration (SIIM-ISIC) Melanoma dataset to validate its effectiveness and its achievements and also put in comparison with several different latest techniques. The results show that the proposed method with 95.46% precision provides the maximum performance among the others.

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Skin Melanoma Segmentation Using Neural Networks Optimized by Quantum Invasive Weed Optimization Algorithm

Cited by 23 publications

References 20 publications

Presentation of a Segmentation Method for a Diabetic Retinopathy Patient’s Fundus Region Detection Using a Convolutional Neural Network

Presentation of a Segmentation Method for a Diabetic Retinopathy Patient’s Fundus Region Detection Using a Convolutional Neural Network

COVID-19 Diagnosis Using Capsule Network and Fuzzy $C$ -Means and Mayfly Optimization Algorithm

An optimal diagnosis system for melanoma dermoscopy images based on enhanced design of horse herd optimizer

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Skin Melanoma Segmentation Using Neural Networks Optimized by Quantum Invasive Weed Optimization Algorithm

Cited by 23 publications

References 20 publications

Presentation of a Segmentation Method for a Diabetic Retinopathy Patient’s Fundus Region Detection Using a Convolutional Neural Network

Presentation of a Segmentation Method for a Diabetic Retinopathy Patient’s Fundus Region Detection Using a Convolutional Neural Network

COVID-19 Diagnosis Using Capsule Network and Fuzzy C -Means and Mayfly Optimization Algorithm

An optimal diagnosis system for melanoma dermoscopy images based on enhanced design of horse herd optimizer

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Product

Resources

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COVID-19 Diagnosis Using Capsule Network and Fuzzy $C$ -Means and Mayfly Optimization Algorithm