An improved AlexNet deep learning method for limb tumor cancer prediction and detection

Abstract

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.

Keywords: AlexNet deep learning; computer-aided diagnosis; convolutional neural network; pathological color image; repetitive phase level set segmentation; synovial sarcoma.