New AI-Based Study Introduces Lightweight Deep Learning Model for Bone Tumour Classification
Prof. Sajid Khan and Prof. Behnam Kiani from the School of Engineering, in collaboration with Dr. Rahim Zakirov from the Medical School, have published a research article titled “Bone-CNN: A Lightweight Deep Learning Architecture for Multi-Class Classification of Primary Bone Tumours in Radiographs” in the Q1-ranked international journal Biomedicines.
The article was published as part of the Special Issue “Applications of Biomedical Engineering and Biomaterials in Human Diseases.”
The study presents Bone-CNN, a lightweight deep learning architecture developed for the multi-class classification of primary bone tumors using radiographic images. The research highlights the practical application of efficient deep learning models in medical imaging and reflects interdisciplinary collaboration between the Engineering and Medical schools, contributing to the advancement of AI-driven diagnostic solutions in healthcare.