A hybrid deep learning approach for skin cancer diagnosis using subband fusion of 3D wavelets

Maniraj, S P; Maran, P. Sardar

doi:10.1007/s11227-022-04371-0

Cited by 18 publications

(14 citation statements)

References 21 publications

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“…e findings indicate that their technique is extremely compatible with human perception [15]. Maniraj and Maran [53] suggested a hybrid deep learning strategy based on 3D wavelet subband fusion. It is a noninvasive, objective way to examine skin photographs.…”

Section: Literature Reviewmentioning

confidence: 83%

FDCNet: Presentation of the Fuzzy CNN and Fractal Feature Extraction for Detection and Classification of Tumors

Molaei

Ghorbani

Dashtiahangar

et al. 2022

Computational Intelligence and Neuroscience

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The detection of brain tumors using magnetic resonance imaging is currently one of the biggest challenges in artificial intelligence and medical engineering. It is important to identify these brain tumors as early as possible, as they can grow to death. Brain tumors can be classified as benign or malignant. Creating an intelligent medical diagnosis system for the diagnosis of brain tumors from MRI imaging is an integral part of medical engineering as it helps doctors detect brain tumors early and oversee treatment throughout recovery. In this study, a comprehensive approach to diagnosing benign and malignant brain tumors is proposed. The proposed method consists of four parts: image enhancement to reduce noise and unify image size, contrast, and brightness, image segmentation based on morphological operators, feature extraction operations including size reduction and selection of features based on the fractal model, and eventually, feature improvement according to segmentation and selection of optimal class with a fuzzy deep convolutional neural network. The BraTS data set is used as magnetic resonance imaging data in experimental results. A series of evaluation criteria is also compared with previous methods, where the accuracy of the proposed method is 98.68%, which has significant results.

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Section: Literature Reviewmentioning

confidence: 83%

FDCNet: Presentation of the Fuzzy CNN and Fractal Feature Extraction for Detection and Classification of Tumors

Molaei

Ghorbani

Dashtiahangar

et al. 2022

Computational Intelligence and Neuroscience

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“…In future, the best filtering and feature extraction research options from the images need to be explored to make sure the prediction accuracy is increased. CNN with filtering and feature extraction [8] 96.39% [11] 98.43% [12] 92.00% [13] 95.26% [14] 91.00% [15] 90.48% [16] 92.30% [17] 98.44% [18] 99.33%…”

Section: Discussionmentioning

confidence: 99%

“…In [18], a hybrid deep learning (HDL) approach was proposed, which used simple median filtering to remove hair, noise, etc. unwanted information and 3D wavelet transform was employed to extract textural information from the dermoscopic image.…”

Section: Cnn With Filtering and Feature Extraction Techniquesmentioning

confidence: 99%

Convolution Neural Network Models to Detect Melanoma: A Review

Naveen¹,

Kumar²,

Haw³

et al. 2022

Proceedings of the International Conference on Computer, Information Technology and Intelligent Computing (CITIC 2022)

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Skin cancer is one of the most serious health issues that humans face. Dermatologists face difficulty in making a skin cancer diagnosis because many skin cancer pigments seem alike. Early detection of skin cancers like Melanoma means a better chance of survival for the patient otherwise it can be life-threatening. For computer vision problems like image classification, deep learning has proven to be the state-of-the-art. There has been a great deal of research into the use of deep learning to automate skin cancer screening. The objective of this paper is to review the state-of-the-art CNN techniques used for Melanoma detection. This paper presents an overview of CNN, followed by analysing the existing work carried out in the area of Melanoma skin cancer detection using Convolution Neural Network (CNN).

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“…Afterward, the authors presented two improved PSO techniques for the optimization of the features. In literature [ 17 ], a Hybrid DL (HDL) system was proposed fusing the sub-band of 3D wavelets. It was a non-invasive and objective system that was used for the inspection of skin images.…”

Section: Literature Reviewmentioning

confidence: 99%

Henry Gas Solubility Optimization Algorithm based Feature Extraction in Dermoscopic Images Analysis of Skin Cancer

Obayya

Alhebri

Maashi

et al. 2023

Cancers

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Artificial Intelligence (AI) techniques have changed the general perceptions about medical diagnostics, especially after the introduction and development of Convolutional Neural Networks (CNN) and advanced Deep Learning (DL) and Machine Learning (ML) approaches. In general, dermatologists visually inspect the images and assess the morphological variables such as borders, colors, and shapes to diagnose the disease. In this background, AI techniques make use of algorithms and computer systems to mimic the cognitive functions of the human brain and assist clinicians and researchers. In recent years, AI has been applied extensively in the domain of dermatology, especially for the detection and classification of skin cancer and other general skin diseases. In this research article, the authors propose an Optimal Multi-Attention Fusion Convolutional Neural Network-based Skin Cancer Diagnosis (MAFCNN-SCD) technique for the detection of skin cancer in dermoscopic images. The primary aim of the proposed MAFCNN-SCD technique is to classify skin cancer on dermoscopic images. In the presented MAFCNN-SCD technique, the data pre-processing is performed at the initial stage. Next, the MAFNet method is applied as a feature extractor with Henry Gas Solubility Optimization (HGSO) algorithm as a hyperparameter optimizer. Finally, the Deep Belief Network (DBN) method is exploited for the detection and classification of skin cancer. A sequence of simulations was conducted to establish the superior performance of the proposed MAFCNN-SCD approach. The comprehensive comparative analysis outcomes confirmed the supreme performance of the proposed MAFCNN-SCD technique over other methodologies.

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A hybrid deep learning approach for skin cancer diagnosis using subband fusion of 3D wavelets

Cited by 18 publications

References 21 publications

FDCNet: Presentation of the Fuzzy CNN and Fractal Feature Extraction for Detection and Classification of Tumors

FDCNet: Presentation of the Fuzzy CNN and Fractal Feature Extraction for Detection and Classification of Tumors

Convolution Neural Network Models to Detect Melanoma: A Review

Henry Gas Solubility Optimization Algorithm based Feature Extraction in Dermoscopic Images Analysis of Skin Cancer

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