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The method replaces the original MobileNet-V3 loss with a bias loss, updates the squeeze-and-excitation module to channel attention, and incorporates cross-layer connections and dilated convolutions to enhance feature learning. Evaluations on the HAM-10000 dataset report 97.84% precision, 96.35% sensitivity, 98.86% accuracy, and 97.32% specificity, indicating improved diagnostic performance and potential reductions in healthcare costs.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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is the main contribution of the proposed model for skin cancer diagnosis?","Question",{"text":63,"@type":64},"The paper proposes a hybrid approach combining Standard U-Net and Improved MobileNet-V3, optimized with hyperparameter search, to improve skin cancer recognition and differentiation between malignant and benign cases.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"Which architectural and training modifications are made to improve performance?",{"text":68,"@type":64},"It replaces the crossed-entropy loss with a bias loss, swaps squeeze-and-excitation with channel attention, adds cross-layer connections across Mobile modules, and incorporates dilated convolutions to enlarge the receptive field.",{"name":70,"@type":61,"acceptedAnswer":71},"How does the model optimize hyperparameters and what optimizer is used?",{"text":72,"@type":64},"The study fine-tunes hyperparameters using Bayesian optimization with a pre-trained MobileNet-V3 CNN as the base for the optimization 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model for skin cancer diagnosis using hybrid U‑Net and improved MobileNet‑V3 with hyperparameters optimization  \nUmesh Kumar Lilhore1, Sarita Simaiya1, Yogesh Kumar Sharma2, Kuldeep Singh Kaswan3, K. B. V. Brahma Rao2, V. V. R. Maheswara Rao4, Anupam Baliyan1, Anchit Bijalwan 5* & Roobaea Alroobaea6  \nSkin 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.  \nAlso 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.  \nKeywords Health care, Convolution Neural Network, Skin cancer, Transfer learning, U-Net, Deep learning  \nAbbreviations  \nDL Deep learning  \nISIC International skin imaging collaboration ADAM Adaptive moment optimization algorithm  \n1Department of Computer Science and Engineering, Chandigarh University, Mohali, Punjab 140413, India. 2Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Greenfield, Vaddeswaram, Guntur, AP, India. 3School of Computing Science and Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India. 4Departmentt of Computer Science and Engineering, Shri Vishnu Engineering College for Women (A), Bhimavaram, India. 5Arba Minch University, Arba Minch, Ethiopia. 6Department of Computer Science, College of Computers and Information Technology, Taif University, P. O. Box 11099, 21944 Taif, Saudi  \nArabia.* email: [drkumarcse@gmail.com](drkumarcse@gmail.com)  \n[www. nature.com/scientificreports/](www. nature.com/scientificreports/)  \nFC Fully connected  \nDL Deep learning  \nCNN Convolution neural network  \nBN Batch normalization  \nSVM Support vector machine ReLu Rectified linear unit  \nTL Transfer learning  \nSkin cancer is considered one of the most","cbCaihgrp1NRk43X","https://ap.wps.com/l/cbCaihgrp1NRk43X","pdf",4695527,24,"English","# Introduction\n## Skin cancer and diagnostic challenges\n## Deep learning and CAD for skin lesion analysis\n## Skin cancer subtypes and related methods\n## Role of transfer learning","[{\"question\":\"What is the main contribution of the proposed model for skin cancer diagnosis?\",\"answer\":\"The paper proposes a hybrid approach combining Standard U-Net and Improved MobileNet-V3, optimized with hyperparameter search, to improve skin cancer recognition and differentiation between malignant and benign cases.\"},{\"question\":\"Which architectural and training modifications are made to improve performance?\",\"answer\":\"It replaces the crossed-entropy loss with a bias loss, swaps squeeze-and-excitation with channel attention, adds cross-layer connections across Mobile modules, and incorporates dilated convolutions to enlarge the receptive field.\"},{\"question\":\"How does the model optimize hyperparameters and what optimizer is used?\",\"answer\":\"The study fine-tunes hyperparameters using Bayesian optimization with a pre-trained MobileNet-V3 CNN as the base for the optimization process.\"}]","A precise model for skin cancer diagnosis using hybrid U-Net and improved MobileNet-V3 with hyperparameters optimization | PDF",1789987411]