In this paper we present a state-of-the-art infrastructure approach to detect and classify oral cancer from the hyperspectral imaging of investigating maxillofacial region. Oral and neck cancer is one of the rampant forms of cancer and this cancer is mostly experienced by socio-economic backward population. The hyperspectral image analysis is emerging as a non-invasive method for classification of cancer. Due to dearth of modern digital tool for computer-aided classification and pre-detection of cancer cells, we have proposed a Deep Boltzmann Machine (DBM) and SVM classification fusion for learning and classifying the pre- and post-cancerous tissue and normal tissue from the hyperspectral imaging. The mixed pixel from background is projected for cancerous region detection. The result of a patient hypercube is presented for the validation of deep learning technique pixel-wise probability map of cancerous and normal healthy tissues on hyperspectral imaging. Moreover, we have obtained a classifier accuracy of 94.75% by classifier fusion by majority voting as compared to conventional classification using the deep learning method imaging technique in hyperspectral image. Hence, the proposed digital pre-screening framework using deep learning classifier fusion on hyperspectral thermal imaging provides a high potential cancer identification tool for socio-economic backward patients in modern healthcare system.

在本文中，我们提出了一种最先进的基础设施方法，通过调查颌面部区域的高光谱成像来检测和分类口腔癌。口腔和颈部癌症是猖獗的癌症之一，这种癌症主要发生在社会经济落后的人群中。高光谱图像分析正在成为一种用于癌症分类的非侵入性方法。由于缺乏用于计算机辅助分类和癌细胞预检测的现代数字工具，我们提出了一种深度玻尔兹曼机 (DBM) 和 SVM 分类融合，用于学习和分类癌前和癌后组织和正常组织高光谱成像。来自背景的混合像素被投射用于癌性区域检测。提出了患者超立方体的结果，以验证高光谱成像上癌变组织和正常健康组织的深度学习技术逐像素概率图。此外，与在高光谱图像中使用深度学习方法成像技术的传统分类相比，我们通过多数表决的分类器融合获得了 94.75% 的分类器准确度。因此，所提出的在高光谱热成像上使用深度学习分类器融合的数字预筛查框架为现代医疗保健系统中社会经济落后的患者提供了一种高潜力的癌症识别工具。与在高光谱图像中使用深度学习方法成像技术的传统分类相比，通过多数投票的分类器融合提高了 75%。因此，所提出的在高光谱热成像上使用深度学习分类器融合的数字预筛查框架为现代医疗保健系统中社会经济落后的患者提供了一种高潜力的癌症识别工具。与在高光谱图像中使用深度学习方法成像技术的传统分类相比，通过多数投票的分类器融合提高了 75%。因此，所提出的在高光谱热成像上使用深度学习分类器融合的数字预筛查框架为现代医疗保健系统中社会经济落后的患者提供了一种高潜力的癌症识别工具。