An improved AlexNet deep learning method for limb tumor cancer prediction and detection.
| Autor: | Perumal A; Department of Biomedical Engineering, Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology, Chennai, 600062, India., Nithiyanantham J; Department of Electronics and Communication Engineering, K.L.N. College of Engineering, Pottapalayam, 630612, India., Nagaraj J; Department of General Surgery, Velammal Medical College Hospital and Research Institute, Madurai, 625009, India. |
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| Jazyk: | angličtina |
| Zdroj: | Biomedical physics & engineering express [Biomed Phys Eng Express] 2024 Nov 05; Vol. 11 (1). Date of Electronic Publication: 2024 Nov 05. |
| DOI: | 10.1088/2057-1976/ad89c7 |
| Abstrakt: | Synovial sarcoma (SS) is a rare cancer that forms in soft tissues around joints, and early detection is crucial for improving patient survival rates. This study introduces a convolutional neural network (CNN) using an improved AlexNet deep learning classifier to improve SS diagnosis from digital pathological images. Key preprocessing steps, such as dataset augmentation and noise reduction techniques, such as adaptive median filtering (AMF) and histogram equalization were employed to improve image quality. Feature extraction was conducted using the Gray-Level Co-occurrence Matrix (GLCM) and Improved Linear Discriminant Analysis (ILDA), while image segmentation targeted spindle-shaped cells using repetitive phase-level set segmentation (RPLSS). The improved AlexNet architecture features additional convolutional layers and resized input images, leading to superior performance. The model demonstrated significant improvements in accuracy, sensitivity, specificity, and AUC, outperforming existing methods by 3%, 1.70%, 6.08%, and 8.86%, respectively, in predicting SS. (© 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.) |
| Databáze: | MEDLINE |
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