Detection and Classification of Melanoma Skin Cancer Using Image Processing Technique

Viknesh, Chandran Kaushik; Kumar, Palanisamy Nirmal; Seetharaman, Ramasamy; Anitha, Devasahayam

doi:10.3390/diagnostics13213313

Cited by 11 publications

(5 citation statements)

References 32 publications

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“…Viknesh et al [ 55 ] provides a computer-aided detection method for melanoma early diagnosis and therapy. Two methods are suggested in this work for the detection of skin cancer.…”

Section: Literature Reviewmentioning

confidence: 99%

DVFNet: A deep feature fusion-based model for the multiclassification of skin cancer utilizing dermoscopy images

Naeem,

Anees

2024

PLoS ONE

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Skin cancer is a common cancer affecting millions of people annually. Skin cells inside the body that grow in unusual patterns are a sign of this invasive disease. The cells then spread to other organs and tissues through the lymph nodes and destroy them. Lifestyle changes and increased solar exposure contribute to the rise in the incidence of skin cancer. Early identification and staging are essential due to the high mortality rate associated with skin cancer. In this study, we presented a deep learning-based method named DVFNet for the detection of skin cancer from dermoscopy images. To detect skin cancer images are pre-processed using anisotropic diffusion methods to remove artifacts and noise which enhances the quality of images. A combination of the VGG19 architecture and the Histogram of Oriented Gradients (HOG) is used in this research for discriminative feature extraction. SMOTE Tomek is used to resolve the problem of imbalanced images in the multiple classes of the publicly available ISIC 2019 dataset. This study utilizes segmentation to pinpoint areas of significantly damaged skin cells. A feature vector map is created by combining the features of HOG and VGG19. Multiclassification is accomplished by CNN using feature vector maps. DVFNet achieves an accuracy of 98.32% on the ISIC 2019 dataset. Analysis of variance (ANOVA) statistical test is used to validate the model’s accuracy. Healthcare experts utilize the DVFNet model to detect skin cancer at an early clinical stage.

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“…Viknesh et al [ 55 ] provides a computer-aided detection method for melanoma early diagnosis and therapy. Two methods are suggested in this work for the detection of skin cancer.…”

Section: Literature Reviewmentioning

confidence: 99%

DVFNet: A deep feature fusion-based model for the multiclassification of skin cancer utilizing dermoscopy images

Naeem,

Anees

2024

PLoS ONE

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“…As per the scientific reports, dangerous UV rays from the sun damage skin cells, resulting in melanoma, thereby increasing the chances of skin cancer. Other factors influencing the development of malignant cells include smoking, alcohol, infections, and the surrounding environment [5].…”

Section: Introductionmentioning

confidence: 99%

Grid-Based Structural and Dimensional Skin Cancer Classification with Self-Featured Optimized Explainable Deep Convolutional Neural Networks

Behara,

Bhero,

Agee

2024

IJMS

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Skin cancer is a severe and potentially lethal disease, and early detection is critical for successful treatment. Traditional procedures for diagnosing skin cancer are expensive, time-intensive, and necessitate the expertise of a medical practitioner. In recent years, many researchers have developed artificial intelligence (AI) tools, including shallow and deep machine learning-based approaches, to diagnose skin cancer. However, AI-based skin cancer diagnosis faces challenges in complexity, low reproducibility, and explainability. To address these problems, we propose a novel Grid-Based Structural and Dimensional Explainable Deep Convolutional Neural Network for accurate and interpretable skin cancer classification. This model employs adaptive thresholding for extracting the region of interest (ROI), using its dynamic capabilities to enhance the accuracy of identifying cancerous regions. The VGG-16 architecture extracts the hierarchical characteristics of skin lesion images, leveraging its recognized capabilities for deep feature extraction. Our proposed model leverages a grid structure to capture spatial relationships within lesions, while the dimensional features extract relevant information from various image channels. An Adaptive Intelligent Coney Optimization (AICO) algorithm is employed for self-feature selected optimization and fine-tuning the hyperparameters, which dynamically adapts the model architecture to optimize feature extraction and classification. The model was trained and tested using the ISIC dataset of 10,015 dermascope images and the MNIST dataset of 2357 images of malignant and benign oncological diseases. The experimental results demonstrated that the model achieved accuracy and CSI values of 0.96 and 0.97 for TP 80 using the ISIC dataset, which is 17.70% and 16.49% more than lightweight CNN, 20.83% and 19.59% more than DenseNet, 18.75% and 17.53% more than CNN, 6.25% and 6.18% more than Efficient Net-B0, 5.21% and 5.15% over ECNN, 2.08% and 2.06% over COA-CAN, and 5.21% and 5.15% more than ARO-ECNN. Additionally, the AICO self-feature selected ECNN model exhibited minimal FPR and FNR of 0.03 and 0.02, respectively. The model attained a loss of 0.09 for ISIC and 0.18 for the MNIST dataset, indicating that the model proposed in this research outperforms existing techniques. The proposed model improves accuracy, interpretability, and robustness for skin cancer classification, ultimately aiding clinicians in early diagnosis and treatment.

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“…The prominent techniques under lossy compression include transform coding [22,23], fractal compression [24,25], chroma sampling [26,27], discrete cosine transform (DCT) [28,29], and the vector quantization algorithm (VQA) [30,31]. On the other hand, lossless compression techniques include run-length encoding [32,33], entropy encoding [34,35], Lempel-Ziv-Welch (LZW) [36,37], and DEFLATE, which synergizes LZSS with Huffman coding [38,39]. Lossless compression is ideal for applications like technical drawings, and medical imaging as it preserves the quality of all original data.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Study of Compression and Texture Integration for Digital Imaging and Communications in Medicine Data Analysis

Shakya,

Vidyarthi

2024

Technologies

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In response to the COVID-19 pandemic and its strain on healthcare resources, this study presents a comprehensive review of various techniques that can be used to integrate image compression techniques and statistical texture analysis to optimize the storage of Digital Imaging and Communications in Medicine (DICOM) files. In evaluating four predominant image compression algorithms, i.e., discrete cosine transform (DCT), discrete wavelet transform (DWT), the fractal compression algorithm (FCA), and the vector quantization algorithm (VQA), this study focuses on their ability to compress data while preserving essential texture features such as contrast, correlation, angular second moment (ASM), and inverse difference moment (IDM). A pivotal observation concerns the direction-independent Grey Level Co-occurrence Matrix (GLCM) in DICOM analysis, which reveals intriguing variations between two intermediate scans measured with texture characteristics. Performance-wise, the DCT, DWT, FCA, and VQA algorithms achieved minimum compression ratios (CRs) of 27.87, 37.91, 33.26, and 27.39, respectively, with maximum CRs at 34.48, 68.96, 60.60, and 38.74. This study also undertook a statistical analysis of distinct CT chest scans from COVID-19 patients, highlighting evolving texture patterns. Finally, this work underscores the potential of coupling image compression and texture feature quantification for monitoring changes related to human chest conditions, offering a promising avenue for efficient storage and diagnostic assessment of critical medical imaging.

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Detection and Classification of Melanoma Skin Cancer Using Image Processing Technique

Cited by 11 publications

References 32 publications

DVFNet: A deep feature fusion-based model for the multiclassification of skin cancer utilizing dermoscopy images

DVFNet: A deep feature fusion-based model for the multiclassification of skin cancer utilizing dermoscopy images

Grid-Based Structural and Dimensional Skin Cancer Classification with Self-Featured Optimized Explainable Deep Convolutional Neural Networks

Comprehensive Study of Compression and Texture Integration for Digital Imaging and Communications in Medicine Data Analysis

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