Deep Convolutional Neural Network for Melanoma Image Classification

Rokhana, Rika; Herulambang, Wiwiet; Indraswari, Rarasmaya

doi:10.1109/ies50839.2020.9231676

Cited by 18 publications

(4 citation statements)

References 9 publications

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“…The created model achieved an accuracy of 86.54% when stored against a dataset during its training phase. To differentiate between benign oral lesions and cancerous melanoma, the authors of [22] developed a deep CNN architecture for this task. A 91.97% sensitivity, 84.76% accuracy, and 787.1% specificity were achieved using the suggested method.…”

Section: Introductionmentioning

confidence: 99%

Advanced Meta-Heuristic Algorithm Based on Particle Swarm and Al-Biruni Earth Radius Optimization Methods for Oral Cancer Detection

Hadjouni

Abdelhamid

El-kenawy³

et al. 2023

IEEE Access

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Oral cancer is a deadly form of cancerous tumor that is widely spread in low and middleincome countries. An early and affordable oral cancer diagnosis might be achieved by automating the detection of precancerous and malignant lesions in the mouth. There are many research attempts to develop a robust machine-learning model that can detect oral cancer from images. However, these are still lacking high precision in oral cancer detection. Therefore, this work aims to propose a new approach capable of detecting oral cancer in medical images with higher accuracy. In this work, a novel and robust oral cancer detection based on a convolutional neural network (CNN) and optimized deep belief network (DBN). The design parameters of CNN and DBN are optimized using a new optimization algorithm, which is developed as a hybrid of Particle Swarm Optimization (PSO) and Al-Biruni Earth Radius (BER) Optimization algorithms and is denoted by (PSOBER). Using a standard biomedical images dataset available on the Kaggle repository, the proposed approach shows promising results outperforming various competing approaches with an accuracy of 97.35%. In addition, a set of statistical tests, such as One-way analysis-of-variance (ANOVA) and Wilcoxon signed-rank tests, are conducted to prove the significance and stability of the proposed approach. The proposed methodology is solid and efficient, and specialists can adopt it. However, additional research on a larger scale dataset is required to confirm the findings and highlight other oral features that can be utilized for cancer detection.

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Section: Introductionmentioning

confidence: 99%

Advanced Meta-Heuristic Algorithm Based on Particle Swarm and Al-Biruni Earth Radius Optimization Methods for Oral Cancer Detection

Hadjouni

Abdelhamid

El-kenawy³

et al. 2023

IEEE Access

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“…In 2018, about 300,000 new cases were recognized [ 8 ]. Based on the Cancer Cell Organization, melanoma cancer with 15000 cases is the fourth most common cancer in the world [ 9 ]. Also, based on this organization, melanoma is the 9 th most common reason for cancer death in 2019 [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Classification for Melanoma Detection Using XceptionNet

Zadeh

2022

Journal of Healthcare Engineering

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Skin cancer is one of the most common types of cancer in the world, accounting for at least 40% of all cancers. Melanoma is considered as the 19th most commonly occurring cancer among the other cancers in the human society, such that about 300,000 new cases were found in 2018. While cancer diagnosis is based on interventional methods such as surgery, radiotherapy, and chemotherapy, studies show that the use of new computer technologies such as image processing mechanisms in processes related to early diagnosis of this cancer can help the physicians heal this cancer. This paper proposes an automatic method for diagnosis of skin cancer from dermoscopy images. The proposed model is based on an improved XceptionNet, which utilized swish activation function and depthwise separable convolutions. This system shows an improvement in the classification accuracy of the network compared to the original Xception and other dome architectures. Simulations of the proposed method are compared with some other related skin cancer diagnosis state-of-the-art solutions, and the results show that the suggested method achieves higher accuracy compared to the other comparative methods.

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“…The testing was performed on 22 images of skin lesions, and the result found the accuracy was 70%. In [4], the skin lesions are divided into different parts, and then, their extracted size, color, and shape were frd to the classifier to categorize different classes, and this process was done by using automatic region growing [4]. This was performed on 60 melanoma images, and this gave an accuracy of 83.3%, sensitivity of 80.0%, and specificity of 86.7%.…”

Section: Introductionmentioning

confidence: 99%

Classification of Skin Lesions Using Deep Convolutional Neural Network

Swain

Rout

Muduli

et al. 2021

Lecture Notes in Electrical Engineering

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The melanoma is a type of skin cancer which develops from melanocytes, responsible to provide the skin color. The severity of melanoma cancer is defined on the basis of different stages which depends upon the depth of penetration and the early detection of melanoma at its prodromal stage is very crucial to stop its advancement. In this work, a novel variant of deep convolutional neural network (DCNN) is developed to perform a binary classification of normal nevus and melanoma by using the dermoscopic images of the PH 2 dataset. Finally, the classification accuracy of the proposed DCNN emerged as the best method to categorize the normal nevus and melanoma with competitive classification accuracy.

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Deep Convolutional Neural Network for Melanoma Image Classification

Cited by 18 publications

References 9 publications

Advanced Meta-Heuristic Algorithm Based on Particle Swarm and Al-Biruni Earth Radius Optimization Methods for Oral Cancer Detection

Advanced Meta-Heuristic Algorithm Based on Particle Swarm and Al-Biruni Earth Radius Optimization Methods for Oral Cancer Detection

Deep Learning-Based Classification for Melanoma Detection Using XceptionNet

Classification of Skin Lesions Using Deep Convolutional Neural Network

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