Enhancement in Skin Cancer Detection using Image Super Resolution and Convolutional Neural Network

Lembhe, Ashutosh; Motarwar, Pranav; Patil, Rudra; Elias, Susan

doi:10.1016/j.procs.2022.12.412

Cited by 22 publications

(16 citation statements)

References 16 publications

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“…The image was subsequently classified using a CNN and an altered variant of Resnet-50. Lembhe et al [ 29 ] used an artificial method for detecting skin cancer using image processing and ML. Image super-resolution (ISR) approaches produce high-resolution images or series from LR images.…”

Section: Resultsmentioning

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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Section: Resultsmentioning

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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“…In recent years, a deep neural network system used transfer learning to extract features from dermoscopy images and a classifier layer to predict class labels. Study [ 49 ] recommends DL for exact lesion extraction. Image quality is improved by “enhanced super-resolution generative adversarial networks (ESRGANs)”.…”

Section: Related Workmentioning

confidence: 99%

An Improved Skin Lesion Classification Using a Hybrid Approach with Active Contour Snake Model and Lightweight Attention-Guided Capsule Networks

Behara,

Bhero,

Agee

2024

Diagnostics

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Skin cancer is a prevalent type of malignancy on a global scale, and the early and accurate diagnosis of this condition is of utmost importance for the survival of patients. The clinical assessment of cutaneous lesions is a crucial aspect of medical practice, although it encounters several obstacles, such as prolonged waiting time and misinterpretation. The intricate nature of skin lesions, coupled with variations in appearance and texture, presents substantial barriers to accurate classification. As such, skilled clinicians often struggle to differentiate benign moles from early malignant tumors in skin images. Although deep learning-based approaches such as convolution neural networks have made significant improvements, their stability and generalization continue to experience difficulties, and their performance in accurately delineating lesion borders, capturing refined spatial connections among features, and using contextual information for classification is suboptimal. To address these limitations, we propose a novel approach for skin lesion classification that combines snake models of active contour (AC) segmentation, ResNet50 for feature extraction, and a capsule network with a fusion of lightweight attention mechanisms to attain the different feature channels and spatial regions within feature maps, enhance the feature discrimination, and improve accuracy. We employed the stochastic gradient descent (SGD) optimization algorithm to optimize the model’s parameters. The proposed model is implemented on publicly available datasets, namely, HAM10000 and ISIC 2020. The experimental results showed that the proposed model achieved an accuracy of 98% and AUC-ROC of 97.3%, showcasing substantial potential in terms of effective model generalization compared to existing state-of-the-art (SOTA) approaches. These results highlight the potential for our approach to reshape automated dermatological diagnosis and provide a helpful tool for medical practitioners.

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“…The detection and classification of skin cancer, a pervasive and potentially life-threatening condition, have witnessed substantial progress due to the integration of cutting-edge technologies, primarily machine learning (ML), deep learning (DL), and image processing techniques. Recent years have seen a surge in studies dedicated to refining algorithms for accurate skin lesion classification, notably the contributions of Hameed et al, Nordmann et al [2], Yang et al [3], and Tan et al [4], significantly advancing this field [1][2][3][4][5][6][7].…”

Section: Enhancing Skin Cancer Classification Using Efficient Net B0-...mentioning

confidence: 99%

Enhancing Skin Cancer Classification using Efficient Net B0-B7 through Convolutional Neural Networks and Transfer Learning with Patient-Specific Data

K J

et al. 2024

Asian Pac J Cancer Prev

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Background: Skin cancer diagnosis challenges dermatologists due to its complex visual variations across diagnostic categories. Convolutional neural networks (CNNs), specifically the Efficient Net B0-B7 series, have shown superiority in multiclass skin cancer classification. This study addresses the limitations of visual examination by presenting a tailored preprocessing pipeline designed for Efficient Net models. Leveraging transfer learning with pre-trained ImageNet weights, the research aims to enhance diagnostic accuracy in an imbalanced multiclass classification context. Methods:The study develops a specialized image preprocessing pipeline involving image scaling, dataset augmentation, and artifact removal tailored to the nuances of Efficient Net models. Using the Efficient Net B0-B7 dataset, transfer learning fine-tunes CNNs with pre-trained ImageNet weights. Rigorous evaluation employs key metrics like Precision, Recall, Accuracy, F1 Score, and Confusion Matrices to assess the impact of transfer learning and fine-tuning on each Efficient Net variant's performance in classifying diverse skin cancer categories. Results: The research showcases the effectiveness of the tailored preprocessing pipeline for Efficient Net models. Transfer learning and fine-tuning significantly enhance the models' ability to discern diverse skin cancer categories. The evaluation of eight Efficient Net models (B0-B7) for skin cancer classification reveals distinct performance patterns across various cancer classes. While the majority class, Benign Kertosis, achieves high accuracy (>87%), challenges arise in accurately classifying Eczema classes. Melanoma, despite its minority representation (2.42% of images), attains an average accuracy of 80.51% across all models. However, suboptimal performance is observed in predicting warts molluscum (90.7%) and psoriasis (84.2%) instances, highlighting the need for targeted improvements in accurately identifying specific skin cancer types. Conclusion: The study on skin cancer classification utilizes EfficientNets B0-B7 with transfer learning from ImageNet weights. The pinnacle performance is observed with EfficientNet-B7, achieving a groundbreaking top-1 accuracy of 84.4% and top-5 accuracy of 97.1%. Remarkably efficient, it is 8.4 times smaller than the leading CNN. Detailed per-class classification exactitudes through Confusion Matrices affirm its proficiency, signaling the potential of EfficientNets for precise dermatological image analysis.

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Enhancement in Skin Cancer Detection using Image Super Resolution and Convolutional Neural Network

Cited by 22 publications

References 16 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

An Improved Skin Lesion Classification Using a Hybrid Approach with Active Contour Snake Model and Lightweight Attention-Guided Capsule Networks

Enhancing Skin Cancer Classification using Efficient Net B0-B7 through Convolutional Neural Networks and Transfer Learning with Patient-Specific Data

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