Hybrid model-based approach for oral cancer detection in distributed cloud environment

Abstract

ABSTRACT Oral cancer is the most fatal disease among the overall malignant tumours worldwide. As more than 50% of the patients are diagnosed with advanced stages of the disease, the oral cancer prognosis is still bad. Early detection is one of the most crucial factors in lowering cancer fatality rates. But there is still a significant obstacle in the form of delayed diagnosis and developing a model for accurate oral cancer detection. To precisely detect oral cancer, deep learning models are used to reduce the number of deaths from cancer. In addition, the diagnosis system based on cloud-deep learning aids the telehealth services more probable. Therefore, this paper proposes a model, termed HMOCD (Hybrid Model based Oral Cancer Detection via Distributed Cloud Environment) with four stages: Pre-processing, segmentation, feature extraction, and detection. The input image is first pre-processed using the Weiner filter, and the output is then provided to CLAHE for enhancing the filtered image. Second, Modified Deep Joint segmentation is applied to the pre-processed image. Following the segmentation process, features for MTH, LGXP, and M-LBP are extracted. Ultimately, a hybrid classification method that integrates models such as Deep Maxout and enhanced Bi-LSTM is utilised to diagnose the condition.