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An Intelligent Mechanism to Detect Multi-Factor Skin Cancer. Diagnostics 2024, 14, 1359. [https://](https://)[ ](https://)[doi.org/10.3390/diagnostics14131359](doi.org/10.3390/diagnostics14131359)  \nReceived: 19 April 2024  \nRevised: 19 June 2024  \nAccepted: 20 June 2024  \nPublished: 26 June 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Computer Sciences, Bahria University Lahore Campus, Lahore 54600, Punjab, Pakistan  \n2 Centre for Artificial Intelligence Research and Optimisation, Design and Creative Technology Vertical, Torrens University Australia, Ultimo, NSW 2007, Australia  \n3 School of Information and Physical Sciences, The University of Newcastle, Newcastle, NSW 2308, Australia  \n* Correspondence: [kamran.shaukat@uon.edu.au](kamran.shaukat@uon.edu.au)  \nAbstract: Deep learning utilizing convolutional neural networks (CNNs) stands out among the state-of-the-art procedures in PC-supported medical findings. The method proposed in this paper consists of two key stages. In the first stage, the proposed deep sequential CNN model preprocesses images to isolate regions of interest from skin lesions and extracts features, capturing the relevant patterns and detecting multiple lesions. The second stage incorporates a web tool to increase the visualization of the model by promising patient health diagnoses. The proposed model was thoroughly trained, validated, and tested utilizing a database related to the HAM 10,000 dataset. The model accomplished an accuracy of 96.25% in classifying skin lesions, exhibiting significant areas of strength. The results achieved with the proposed model validated by evaluation methods and user feedback indicate substantial improvement over the current state-of-the-art methods for skin lesion classification (malignant/benign) . In comparison to other models, sequential CNN surpasses CNN transfer learning (87.9%), VGG 19 (86%), ResNet-50 + VGG-16 (94.14%), Inception v3 (90%), Vision Transformers (RGB images) (92.14%), and the Entropy-NDOELM method (95.7%) . The findings demonstrate the potential of deep learning, convolutional neural networks, and sequential CNN in disease detection and classification, eventually revolutionizing melanoma detection and, thus, upgrading patient consideration.  \nKeywords: melanoma; convolutional neural networks; deep learning; skin lesions; deep learning; machine learning; intelligent tool  \n1. Introduction  \nMulti-type cancer is possibly the most dangerous disease and represents an immense risk to humans [1] . Among the most dangerous cancers is melanoma skin cancer, which is known to be fatal on the little chance that it is not recognized early. Early analysis can considerably lessen death rates and limit the complications of treatment. Generally, the finding system includes a biopsy, where an example is taken from the patient’s skin and inspected by a dermatologist. Accuracy of this assessment relies upon the specialist’s ability and the available apparatuses. Skin cancer is a significant public health concern worldwide. According to the latest estimates, there were approximately 8114 new cases of skin melanoma in China and 99,935 in the United States in 2022 . There are also 554,274 patients in China and 554,274 in the United States [1] .  \nThe above statistics show the critical increment in daily cases, so exact determination frequently requires both experience and high-level methods to improve accuracy. Having exact instruments withi","cbCaiupMZUP2ICEY","https://ap.wps.com/l/cbCaiupMZUP2ICEY","pdf",7134267,21,"English","# Introduction\n## Motivation for early melanoma detection\n## Role of AI and deep learning in diagnostic support","[{\"question\":\"What are the two main stages of the proposed detection method?\",\"answer\":\"The method first uses a deep sequential CNN to preprocess images, isolate regions of interest, and extract features to detect multiple lesions. It then adds a web tool to improve visualization and support patient health diagnosis.\"},{\"question\":\"How was the model evaluated and what dataset was used?\",\"answer\":\"The model was trained, validated, and tested using a database related to the HAM 10,000 dataset.\"},{\"question\":\"What accuracy does the model achieve for skin lesion classification?\",\"answer\":\"The proposed model reaches 96.25% accuracy in classifying skin lesions as malignant or benign.\"}]","An Intelligent Mechanism to Detect Multi-Factor Skin Cancer - Action: deep learning sequential CNN diagnostics for melanoma detection | PDF",1790042279,53]