An attention-based bidirectional long short-term memory based optimal deep learning technique for bone cancer detection and classifications

Abstract

Bone cancer detection is an essential region of medical analysis but developments in medical imaging and artificial intelligence (AI) are vital. Using approaches, namely deep learning (DL) and machine learning (ML), radiologists and medical staff can examine X-ray, CT, and MRI scans to identify bone cancer and abnormalities. These technologies support earlier diagnosis, correct diagnosis, and treatment planning, enhancing patient solutions. The combination of AI-driven image analysis and the knowledge of medical practitioners improves the speed and precision of bone cancer detection, contributing to more effectual clinical activities. DL algorithms, particularly CNNs, are exposed to great performance in image classification tasks and are extremely utilized for medical image analysis. We offer a Hybrid Rice Optimization Algorithm with DL-Assisted Bone Cancer Detection (HROADL-BCD) technique on medical X-ray images. The major intention of the HROADL-BCD method is to examine the X-ray images for the recognition of bone cancer. In the presented HROADL-BCD method, a bilateral filtering (BF) process was performed to remove the noise. To derive feature vectors, the HROADL-BCD technique applied the EfficientNet model. The HROADL-BCD technique involved the HROA for hyperparameter tuning of the EfficientNet model. Last, the bone cancer detection and classification process were executed by the attention-based bidirectional long short-term memory (ABiLSTM) approach. A wide range of simulations could be applied for the simulation result analysis of the HROADL-BCD algorithm. The extensive outcome of the HROADL-BCD approach inferred the superior outcome of 97.62% outcome concerning various aspects.

Keywords

• medical imaging
• bone cancer
• computer-aided diagnosis
• rice optimization algorithm
• deep learning