Deep Learning-Driven Oral Cancer Prediction Using Mobilenet And Inception V3

Oral cancer is a global health concern with high death rates and late diagnosis, predominantly prevalent in low- and middle-income countries due to the unavailability of state-of-the-art diagnostic methods. Early diagnosis calls for enhanced survival, whereas conventional diagnostic procedures rely on expert judgment that is tedious, unpredictable, and not amenable to adoption in less-well-equipped health facilities. Present deep learning methods such as Faster R-CNN are inefficient to detect highly complex lesions varying in sizes, shapes, and textures and ultimately leads to misdiagnosis more frequently. This paper introduces a hybrid deep framework using MobileNet and Inception v3 as a solution for all the aforesaid problems towards an automated diagnosis for oral cancer. MobileNet is designed with efficient and lightweight computation and is thus best utilized in real-time applications, while Inception v3 improves deep feature extraction and thus classification accuracy of lesions. Our combined model can attain a very high classification rate of 94% by taking the best out of both the architectures and making precise and rapid cancer diagnosis. The low-cost, scalable, and automated nature of this system facilitates early diagnosis, instant medical intervention, and a dramatic reduction in mortality rates. The technique is especially helpful in underdeveloped healthcare areas where special medical information is inaccessible. By revealing its potential to be more broadly applied in medical imaging, this kind of AI technology opens the door to future deep learning-based health solutions.