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Optimised EfficientNet for multi-class skin cancer diagnosis: integrating chaotic grey wolf algorithms (Record no. 131787)
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| fixed length control field | 02402nam a2200133 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
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| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | P. Rama |
| 245 ## - TITLE STATEMENT | |
| Title | Optimised EfficientNet for multi-class skin cancer diagnosis: integrating chaotic grey wolf algorithms |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | P 728-736 |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. biblio.abstract | The detection of skin cancer holds paramount importance worldwide due to its impact on global health.<br/>While deep convolutional neural networks (DCNNs)<br/>have shown potential in this domain, current approaches often struggle with fine-grained variability in<br/>skin lesion features, imbalanced datasets and inadequate<br/>augmentation. The objectives of our study encompass<br/>building a model that not only enhances accuracy but<br/>also reduces training time and cost, improves dimensionality reduction during classification and segmentation, and is evaluated on larger datasets for robustness.<br/>We enhance the learning process using an improved<br/>nature-inspired optimisation algorithm tailored specifically for skin cancer classification tasks. This algorithm<br/>optimises model parameters to maximise classification<br/>accuracy while minimising computational overhead.<br/>Our proposed architecture leverages a gold standard<br/>dermatological image dataset meticulously curated and<br/>validated by experts. This dataset spans eight distinct<br/>classes, including actinic keratosis, basal cell carcinoma, melanoma, nevus, and others, ensuring a diverse<br/>representation of skin conditions. The balanced class distribution in the training and test sets enables fair evaluation and robustness assessment of our deep learning<br/>models. The developed algorithm showcases novelty in its<br/>approach, contributing to advancements in early skin<br/>cancer detection technologies. This study introduces an<br/>optimised deep learning model designed for the early<br/>detection of skin cancer, achieving a notable accuracy of<br/>97.48%. Our study aims to contribute significantly to the<br/>medical field by providing a reliable and efficient tool<br/>for dermatologists and healthcare practitioners to assist<br/>in diagnosing skin conditions accurately and promptly,<br/>ultimately improving patient outcomes and healthcare<br/>efficiency. |
| 654 ## - SUBJECT ADDED ENTRY--FACETED TOPICAL TERMS | |
| Subject | <a href="Actinic keratosis">Actinic keratosis</a> |
| -- | <a href="skin cancer detection">skin cancer detection</a> |
| -- | <a href="segmentation">segmentation</a> |
| -- | <a href=" deep convolutional neural networks"> deep convolutional neural networks</a> |
| -- | <a href="classification">classification</a> |
| -- | <a href="basal cell carcinoma">basal cell carcinoma</a> |
| 773 0# - HOST ITEM ENTRY | |
| Host Biblionumber | 125299 |
| Host Itemnumber | 112524 |
| Title | Current Science |
| International Standard Serial Number | 0011-3891 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Journal Article |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Location (home branch) | Sublocation or collection (holding branch) | Date acquired | Koha issues (times borrowed) | Piece designation (barcode) | Koha date last seen | Price effective from | Koha item type |
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| Dewey Decimal Classification | SNDT Juhu | SNDT Juhu | 24/06/2025 | JP672.9 | 24/06/2025 | 24/06/2025 | Journal Article |