Abstract
Bone malignant tumors are one of the important health problems because tumors are formed due to the affectedness of the healthiest bone tissues. This serious bone cancer has been identified with the help of the different risk factors such as chills, swear symptoms, swelling, bones weaken risk, and night swears symptoms. These symptoms are not easy to predict in beginning stage with accurate manner. So, automatic bone cancer detection system has been developed to predict the cancer in earlier sate. Initially, the bone images are collected from patient, and noise in the images is eliminated using median filter. After eliminating the noise, affected tumor part is detected by applying the intuitionistic fuzzy rank correlation. From the detected intuitionistic fuzzy-based clustered images, different statistical features are extracted. The derived features are processed by applying the deep neural networks layers that successfully examines each features using Levenberg-Marquardt learning algorithm. The successful learning process predicts bone cancer-related features with accurate manner (99.1%). Finally, excellence of bone cancer prediction system is analyzed using MATLAB-based experimental setup and performance metrics such as F1 score, accuracy, error rate and so on.