Oral cancer is one of the common cancer types which scales higher in death rate every year. The connectivity of two different cavities like oral cavity and nasal cavity is known as Upper Aero-Digestive Tract (UADT). Both oral and nasal cavities consist of thirteen connecting sites from mouth to upper stomach. The traditional pathological analysis like manual microscopic review brings out major intra and interobserver variability problem. A new automated system is proposed using computer vision techniques to focus and analyse major pathological problems like intra and interobserver variability problem and mis-classification of dysplasia type of tumours. The morphological behaviour of biopsy tissue samples are analysed digitally with different sites of UADT and different cancerous and non-cancerous stages. The proposed technique will play a major role in assisting the manual pathology procedure for analysing the morphology of dysplasia type of tumours and classification of tumour gradings. A method is proposed which integrates an alternate process to find the morphology of dysplasia type tumours using different image processing techniques. A state-of-the-art Force Reconstructed Particle Swarm Optimization Based SVM is proposed for UADT oral cancer classification for ten different oral cavity sites. The proposed classification technique achieved 94 % accuracy.

口腔癌是每年死亡率更高的常见癌症类型之一。两种不同的腔体（如口腔和鼻腔）的连通性被称为上消化道（UADT）。口腔和鼻腔均由从嘴到上胃的13个连接部位组成。传统的病理分析（如手动显微镜检查）带来了主要的观察者内部和观察者之间的变异性问题。提出了一种使用计算机视觉技术的新自动化系统，以集中和分析主要病理问题，例如观察者内部和观察者之间的变异性问题以及肿瘤的发育不良类型的错误分类。用UADT的不同部位以及不同的癌变阶段和非癌变阶段对活检组织样品的形态学行为进行数字分析。所提出的技术将在协助人工病理过程中分析不典型增生类型的肿瘤的形态和肿瘤分级的分类中发挥重要作用。提出了一种方法，该方法利用不同的图像处理技术整合了替代过程，以发现异型增生型肿瘤的形态。提出了一种基于力重构粒子群算法的最新技术，用于十个不同口腔部位的UADT口腔癌分类。提出的分类技术达到94％的准确度。提出了一种基于力重构粒子群算法的最新技术，用于十个不同口腔部位的UADT口腔癌分类。提出的分类技术达到94％的准确度。提出了一种基于力重构粒子群算法的最新技术，用于十个不同口腔部位的UADT口腔癌分类。提出的分类技术达到94％的准确度。