Enhanced Bone Cancer Diagnosis through Deep Learning on Medical Imagery DOI Open Access

M. Venkata Ramana,

P. N. Siva Jyothi,

S. G. Anuradha

и другие.

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2025, Номер 11(1)

Опубликована: Фев. 12, 2025

Bone cancer, especially osteosarcoma, is an aggressive tumor with a highly complex histopathologic appearance that imposes considerable diagnostic difficulties. Although practical and efficient, traditional methods current deep learning models have class imbalance, fused pixel intensity distributions, tissue heterogeneity hinder efficiency. These problems emphasize the demand of more sophisticated frameworks specifically address distinct properties bone cancer histopathology images. To overcome these shortcomings, in this study proposes framework, IBCDNet, to alleviate limitations. Inspired by cutting-edge improvements architecture (e.g., like attention, residual connections, proposed Intelligent Learning-Based Cancer Detection (ILB-BCD) algorithm), framework combines different features from both public private datasets efficient way. This allows for strong feature extraction, better imbalanced data, thus precise classification. The model obtains state-of-the-art results 98.39% on Osteosarcoma Tumor Assessment Dataset, outperforming powerful baseline ResNet50, DenseNet121, InceptionV3. further affirms its robustness respective precision (97.8%), recall (98.1%), F1-score (98.0%) which shows remarkable improvement We present cost-effective scalable real-world clinical applications assist pathologists early detection accurate diagnosis cancer. Those important gaps identified addressed research contribute progress towards AI-driven healthcare global goals medicine enhanced patient outcomes.

Язык: Английский

Enhanced Bone Cancer Diagnosis through Deep Learning on Medical Imagery DOI Open Access

M. Venkata Ramana,

P. N. Siva Jyothi,

S. G. Anuradha

и другие.

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2025, Номер 11(1)

Опубликована: Фев. 12, 2025

Bone cancer, especially osteosarcoma, is an aggressive tumor with a highly complex histopathologic appearance that imposes considerable diagnostic difficulties. Although practical and efficient, traditional methods current deep learning models have class imbalance, fused pixel intensity distributions, tissue heterogeneity hinder efficiency. These problems emphasize the demand of more sophisticated frameworks specifically address distinct properties bone cancer histopathology images. To overcome these shortcomings, in this study proposes framework, IBCDNet, to alleviate limitations. Inspired by cutting-edge improvements architecture (e.g., like attention, residual connections, proposed Intelligent Learning-Based Cancer Detection (ILB-BCD) algorithm), framework combines different features from both public private datasets efficient way. This allows for strong feature extraction, better imbalanced data, thus precise classification. The model obtains state-of-the-art results 98.39% on Osteosarcoma Tumor Assessment Dataset, outperforming powerful baseline ResNet50, DenseNet121, InceptionV3. further affirms its robustness respective precision (97.8%), recall (98.1%), F1-score (98.0%) which shows remarkable improvement We present cost-effective scalable real-world clinical applications assist pathologists early detection accurate diagnosis cancer. Those important gaps identified addressed research contribute progress towards AI-driven healthcare global goals medicine enhanced patient outcomes.

Язык: Английский

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