Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/4044 2018 2018 Collage of Computer Science and Information Technology, University of Anbar, Rmadi, Iraq; Collage of Computer Science and Information Technology, University of Anbar, Rmadi, Iraq Murtadha Murtadha Collage of Computer Science and Information Technology, University of Anbar, Rmadi, Iraq Murtadha M. Hamad Collage of Computer Science and Information Technology, University of Anbar, Rmadi, Iraq Azmi Tawfeq Hussein Alrawi   This paper applies deep learning techniques in classifying X-ray images to detect osteoporosis. Osteoporosis, a bone weakness condition, increases the risk of fractures; therefore, accurate early diagnosis is essential in management. We have designed a hybrid method called Fuzzy Logic Preprocessed Convolutional Neural Network, or FLPCNN, wherein fuzzy logic is used at the preprocessing step to handle uncertainty and imprecision of features extracted from X-ray images. This paper used a dataset of X-ray images, and the FLPCNN model was applied, classifying them into osteoporotic and non-osteoporotic with quite an accuracy of 100%. Fuzzy logic preprocessing combined with Convolutional Neural Networks (CNN) enhances the model’s classification accuracy and interpretable decisions. The proposed method would be a new way to cut down diagnostic errors and improve patient outcomes, opening ways for further research into deep learning techniques applied in healthcare. 2026 2026 01 21 10.54216/FPA.210201 https://www.americaspg.com/articleinfo/3/show/4044