A Hybrid Deep Learning Network for Skin Lesion Extraction

Rout, Ranjita; Parida, Priyadarsan; Dash, Sonali

doi:10.1007/978-3-031-27524-1_66

Cited by 3 publications

(3 citation statements)

References 23 publications

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“…The proposed approach is superior, as demonstrated by the experimental findings, which show an average accuracy of 93% and 96% for the ISIC 2016 and ISIC 2018 datasets, respectively. [68] By merging the DeepLabV3 + with several base networks, including ResNet-50, ResNet-18, and MobileNetV2, the research proposes a unique convolutional neural network (CNN)-based deep learning technique. By not requiring images to be preprocessed, the suggested model reduces the algorithm's complexity.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The model was tested on publically available datasets ISIC 2018, DermIS, and HP2 and achieved Dice 89.3 on ISIC 2018 dataset, Dice 87.9% on DermIS, and PH2 an average Dice was 92.3%. [68] In this research, the researcher proposed an explainable CNN-based stacked ensemble framework for early-stage melanoma detection. In his proposed model, multiple CNN sub-models with transfer learning techniques assembled the same classification task.…”

Section: Introductionmentioning

confidence: 99%

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Improved Segmentation Model for Melanoma Lesion Detection Using Normalized Cross-Correlation-Based k-Means Clustering

Faizi,

Adnan

2024

IEEE Access

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The most unexpected and fatal kind of cancer, melanoma, has been on the rise in its spread to different parts of the body. Early detection can significantly reduce its fatality rate. The primary stages of melanoma for its identification by the unaided eye are a difficult task that demands extensive training and understanding. Due to a lack of qualified dermatologists, a computerized and automated method is required to correctly detect melanoma. This study achieved this feat through a proposed system that can effectively detect and classify melanoma as benign or malignant. The process begins with image template matching by using normalized cross-correlation technique induction to mark the infected area of skin lesion as the region of interest (ROI) from ISIC datasets 2017, 2019, and 2020 dermoscopic images. Our novel model dynamically calculated number of clusters is assigned to the k-means clustering algorithm, and ROI is extracted. Histogram equalization is applied to the output image for contrast enhancement. Hu Moment method is implemented for shape classification and part recognition from the segmented image. GLCMbased Haralick feature extractor is used in the proposed system to extract textural features generating the feature vector from the segmented skin lesion. It leads the classifier to identify skin lesions as cancerous or non-cancerous. Random Forest, Decision Tree, Support Vector Machine (SVM), Gaussian Naïve Bayes, Logistic Regression, and K-Nearest Neighbor (KNN) Classifiers are used for classification. The KNN, SVM and Random Forest was successful to attain the highest accuracy of 99.29%, 99.38% and 99.46% on the given dermoscopic images datasets ISIC-2019, ISIC-2020 and ISIC-2017 respectively. The proposed method, normalized cross correlation-based k-means clustering model, was found to be more robust and accurate than existing methods and incorporates much more feature information from the images.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improved Segmentation Model for Melanoma Lesion Detection Using Normalized Cross-Correlation-Based k-Means Clustering

Faizi,

Adnan

2024

IEEE Access

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“…Yet, in recent years, there have been notable improvements in computer hardware and software systems as well as a sharp rise in the volume and complexity of data [22]. Deep learning techniques for precise and accurate medical image analysis have proliferated and improved as a result of these breakthroughs [23].…”

Section: Introductionmentioning

confidence: 99%

Improved Tuna Swarm-based U-EfficientNet: Skin Lesion Image Segmentation by Improved Tuna Swarm Optimization

Ahmed¹,

Jalil²,

Usman³

2023

IJACSA

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Skin cancers have been on an upward trend, with melanoma being the most severe type. A growing body of investigation is employing digital camera images to computeraided examine suspected skin lesions for cancer. Due to the presence of distracting elements including lighting fluctuations and surface light reflections, interpretation of these images is typically difficult. Segmenting the area of the lesion from healthy skin is a crucial step in the diagnosis of cancer. Hence, in this research an optimized deep learning approach is introduced for the skin lesion segmentation. For this, the EfficientNet is integrated with the UNet for enhancing the segmentation accuracy. Also, the Improved Tuna Swarm Optimization (ITSO) is utilized for adjusting the modifiable parameters of the U-EfficientNet to minimize the information loss during the learning phase. The proposed ITSU-EfficientNet is assessed based on various evaluation measures like Accuracy, Mean Square Error (MSE), Precision, Recall, IoU, and Dice Coefficient and acquired the values are 0.94, 0.06, 0.94, 0.94, 0.92 and 0.94 respectively.

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HEXA-GAN: Skin lesion image inpainting via hexagonal sampling based generative adversarial network

Bansal,

Sridhar

2024

Biomedical Signal Processing and Control

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A Hybrid Deep Learning Network for Skin Lesion Extraction

Cited by 3 publications

References 23 publications

Improved Segmentation Model for Melanoma Lesion Detection Using Normalized Cross-Correlation-Based k-Means Clustering

Improved Segmentation Model for Melanoma Lesion Detection Using Normalized Cross-Correlation-Based k-Means Clustering

Improved Tuna Swarm-based U-EfficientNet: Skin Lesion Image Segmentation by Improved Tuna Swarm Optimization

HEXA-GAN: Skin lesion image inpainting via hexagonal sampling based generative adversarial network

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