Sand Cat Swarm Optimization with Deep Transfer Learning for Skin Cancer Classification

Anupama, C. S. S.; Yonbawi, Saud; Gummadi, Jose Moses; Lydia, E. Laxmi; Kadry, Seifedine; Kim, Jung-Eun

doi:10.32604/csse.2023.038322

Cited by 2 publications

(1 citation statement)

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“…The article 12 presents research on the utilization of YOLOv4-DarkNet along with active contour techniques to localize and segment melanoma. The method was evaluated on the International Skin Imaging Collaboration (ISIC) datasets for 2016 and 2018.…”

Section: Literature Reviewmentioning

confidence: 99%

A precise model for skin cancer diagnosis using hybrid U-Net and improved MobileNet-V3 with hyperparameters optimization

Kumar Lilhore,

Simaiya,

Sharma

et al. 2024

Sci Rep

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Skin cancer is a frequently occurring and possibly deadly disease that necessitates prompt and precise diagnosis in order to ensure efficacious treatment. This paper introduces an innovative approach for accurately identifying skin cancer by utilizing Convolution Neural Network architecture and optimizing hyperparameters. The proposed approach aims to increase the precision and efficacy of skin cancer recognition and consequently enhance patients' experiences. This investigation aims to tackle various significant challenges in skin cancer recognition, encompassing feature extraction, model architecture design, and optimizing hyperparameters. The proposed model utilizes advanced deep-learning methodologies to extract complex features and patterns from skin cancer images. We enhance the learning procedure of deep learning by integrating Standard U-Net and Improved MobileNet-V3 with optimization techniques, allowing the model to differentiate malignant and benign skin cancers. Also substituted the crossed-entropy loss function of the Mobilenet-v3 mathematical framework with a bias loss function to enhance the accuracy. The model's squeeze and excitation component was replaced with the practical channel attention component to achieve parameter reduction. Integrating cross-layer connections among Mobile modules has been proposed to leverage synthetic features effectively. The dilated convolutions were incorporated into the model to enhance the receptive field. The optimization of hyperparameters is of utmost importance in improving the efficiency of deep learning models. To fine-tune the model's hyperparameter, we employ sophisticated optimization methods such as the Bayesian optimization method using pre-trained CNN architecture MobileNet-V3. The proposed model is compared with existing models, i.e., MobileNet, VGG-16, MobileNet-V2, Resnet-152v2 and VGG-19 on the “HAM-10000 Melanoma Skin Cancer dataset". The empirical findings illustrate that the proposed optimized hybrid MobileNet-V3 model outperforms existing skin cancer detection and segmentation techniques based on high precision of 97.84%, sensitivity of 96.35%, accuracy of 98.86% and specificity of 97.32%. The enhanced performance of this research resulted in timelier and more precise diagnoses, potentially contributing to life-saving outcomes and mitigating healthcare expenditures.

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Section: Literature Reviewmentioning

confidence: 99%

A precise model for skin cancer diagnosis using hybrid U-Net and improved MobileNet-V3 with hyperparameters optimization

Kumar Lilhore,

Simaiya,

Sharma

et al. 2024

Sci Rep

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Automated Skin Cancer Detection and Classification using Cat Swarm Optimization with a Deep Learning Model

Rajendran,

Shanmugam

2024

Eng. Technol. Appl. Sci. Res.

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The application of Computer Vision (CV) and image processing in the medical sector is of great significance, especially in the recognition of skin cancer using dermoscopic images. Dermoscopy denotes a non-invasive imaging system that offers clear visuals of skin cancers, allowing dermatologists to analyze and identify various features crucial for lesion assessment. Over the past few years, there has been an increasing fascination with Deep Learning (DL) applications for skin cancer recognition, with a particular focus on the impressive results achieved by Deep Neural Networks (DNNs). DL approaches, predominantly CNNs, have exhibited immense potential in automating the classification and detection of skin cancers. This study presents an Automated Skin Cancer Detection and Classification method using Cat Swarm Optimization with Deep Learning (ASCDC-CSODL). The main objective of the ASCDC-CSODL method is to enforce the DL model to recognize and classify skin tumors on dermoscopic images. In ASCDC-CSODL, Bilateral Filtering (BF) is applied for noise elimination and U-Net is employed for the segmentation process. Moreover, the ASCDC-CSODL method exploits MobileNet for the feature extraction process. The Gated Recurrent Unit (GRU) approach is used for the classification of skin cancer. Finally, the CSO algorithm alters the hyperparameter values of GRU. A wide-ranging simulation was performed to evaluate the performance of the ASCDC-CSODL model, demonstrating the significantly improved results of the ASCDC-CSODL model over other approaches.

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Sand Cat Swarm Optimization with Deep Transfer Learning for Skin Cancer Classification

Cited by 2 publications

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A precise model for skin cancer diagnosis using hybrid U-Net and improved MobileNet-V3 with hyperparameters optimization

A precise model for skin cancer diagnosis using hybrid U-Net and improved MobileNet-V3 with hyperparameters optimization

Automated Skin Cancer Detection and Classification using Cat Swarm Optimization with a Deep Learning Model

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