Automatic detection of oral cancer in smartphone-based images using deep learning for early diagnosis

Oral cancer is one of the most common cancers worldwide, with high mortality rates. According to the International Agency for Research on Cancer, there were an estimated 377,000 new cases of lip and oral cavity cancers in 2020, with nearly 177,000 deaths worldwide.1 Despite advances in oncology therapy, mortality rates for oral cancer remain high over the past few decades. A majority of oral cancer patients do not have access to timely, quality diagnosis, and treatment, especially in rural areas, resulting in poor survival rates. The overall 5-year survival rate of diagnosed oral cancer patients is around 50% and has varied by race and area,2 whereas survival rates as high as 65% have been reported in developed countries; in some rural areas, they can be as low as 15% depending on the affected part of the oral cavity. The 2020 Cancer Statistics Report from India estimated that 66.6% of patients suffering from head and neck cancer were diagnosed at the locally advanced stage.3 In short, patient survival rates and prognosis are severely compromised when oral cancer patients are diagnosed at more advanced stages4,5 so that enhancing early diagnosis could mean a significant rise in positive survival outcomes.6,7 As oral squamous cell carcinoma (OSCC) accounts for ∼90% of all oral cancer,8,9 these two terms tend to be used interchangeably. OSCC, which originates as an epithelial dysplasia (from a histopathologic perspective), generally develops from precursor lesions termed as oral potentially malignant disorders (OPMDs).10,11 Nevertheless, it is not inevitable that all OPMDs, even the most commonly encountered such as oral lichen planus, leukoplakia, and erythroplakia,11 result in the subsequent development of malignancies.10,12 Diagnosing OPMDs as definable diseases is also challenging due to the numerous varieties, various forms, and overlapping features. However, studies10,13,14 have found that when an OPMD changes to a nonhomogeneous presentation, it is more likely to be considered as an adverse progression, in other words, nonhomogeneous lesions have a greater risk of malignant transformation as against homogeneous lesions. Hence, compared with defined diagnosis, distinguishing the potential malignant characteristics of OPMD is a greater concern. In summary, an ideal clinical prediction method should be employed to diagnose OSCC early and in particular to assess malignancy at the OPMD stage. Currently, conventional oral examination (COE) consisting of visual and tactile assessment (followed by tissue biopsy if there are any suspicious findings) is the most routine procedure in the management of oral cancer and precursor disease. However, one limitation for COE is that several features of oral cancer may appear benign and even mimic aphthous ulcers, and they are too clinically heterogeneous and subtle for general dentists to distinguish. Second, because of its invasive nature and sampling bias that can lead to underdiagnosis or misdiagnosis, biopsy is often not ideal as a screening tool.15 Furthermore, although specialists can recognize most of the characteristics that differentiate benign and cancerous lesions, the number of specialists and health resources are limited and concentrated across regions, causing a large part of the oral cancer burden to fall on low-resource communities. Therefore, the idea of establishing a cost-effective screening strategy as an adjunctive aid to the current procedures is gaining widespread popularity.16 Recently, deep learning techniques have exhibited a comparative advantage over feature-based methods in medical image analysis. A variety of studies17,18 showed that deep learning algorithms are able to surpass the performance of human experts in many disease recognition scenarios. In oral cancer diagnosis, deep learning methods also showed promising results for automatic analysis of pathology, confocal laser endomicroscopy (CLE) images, and fluorescence images. For example, Kumar et al.19 proposed a two-stage method that used a segmentation network and a random forest tree classifier to identify different stages of oral cancer in histological images. Aubreville et al.20 tested the deep convolutional neural network for OSCC diagnosis on CLE images, and the results showed that it outperformed the feature-based classification methods. Song et al.21,22 developed mobile connected devices to acquire fluorescence oral images and used them to identify oral disease. However, these methods all require expensive devices or a specifically designed screening platform, which are not accessible to everyone. In other words, patients are still required to go a professional clinic to receive disease diagnosis. With the rapid development of both imaging and sensing technologies in camera systems, the ubiquity of smartphones is equipped with higher quality, low-noise, and faster camera modules. Smartphone-based white light inspection methods23–25 are good solutions for acquiring oral images. Camalan et al.23 used a CNN-based network for classifying white light images as normal or suspicious; however, the patient sample is very limited: with only 54 cases. To build a reliable system, Welikala et al.24 collected more clinically labeled data for network training and evaluation, achieving a sensitivity of 52.13% and a specificity of 49.11% on multiclass classification task. However, we found that the clinical imaging capturing from hand-held smartphone cameras may exhibit a large variability, leading to poor diagnostic performance of the detection algorithm. For example, from a computer vision point of view, the shape and size of imaging lesions depend on the fields-of-view and focal distance, respectively. Unfortunately, a limited amount of research focuses on this problem. To address these challenges, we aim to explore reliable and robust smartphone-based white light image approaches, including an image-capturing method, resampling method, and high performance of CNN model, for oral disease recognition. Our main contributions are summarized as follows. 1. We propose a simple yet effective image-capturing method for consistent lesion position and focal distance over different images. The method allows direct focus on discriminative parts for disease recognition, without utilizing any region proposal methods,24,25 or relying on bounding box annotation24 by oral specialists. 2. We present a resampling method to alleviate the effect of image variability that introduced by the hand-held smartphone camera; at the same time, it can remedy the class imbalance problem. 3. We use one of the latest proposed convolutional neural networks (HRNet) for oral disease classification and achieve better results than common classification models. Analysis of the model’s performance on our collected images shows that common imaging pattern on a smartphone is a valuable approach for the early diagnosis. 2. Materials and Methods 2.1. Image-Capturing Method The focal length of the main camera in a smartphone is commonly short, for example, the iPhone 12 has a wide-angle (only 26 mm) camera. The focal length number tells us how much of the scene is captured in the picture, and the lower the number is, the wider the view is. Since most lesions are relatively small, we cannot capture the photograph where the lesion occupies most areas of the image. This means that the captured image may have many irrelevant backgrounds. In addition, even with the same lesion, the size of the imaging lesion may vary with different distances between the camera and lesion or using different cameras with different focal lengths. Thus these would introduce the large variability to the system’s performance going forward, and we need to find a method to reduce this considerable variability. Although identifying oral disease is very difficult for the nonmedical expert, locating the position of oral lesions is relatively easy because the visual appearance of normal and diseased tissue is significantly different. We use the camera grid to assist in locating the lesion in the center of image and to keep each area of lesion in the images neither too small nor too big. Thus it is possible to use the fixed region of interest (ROI) method to crop the discriminative parts and filter the irrelevant backgrounds, without utilizing any region proposal methods or relying on any manually cropped methods. This particular positioning of the main object in an image is helpful to improve the performance of CNN for image recognition. We use the native phone’s camera app to capture the oral cavity image. As shown in Fig. 1, the camera grid helps us to see if the lesion is properly placed at the center for optimal balance in the shot. The operation can be easily done by a person using a hand-held smartphone camera. The basic steps are as follows. Open in a separate window Fig. 1 Illustration of data acquisition. The aspect ratio is set 4:3; the image shows a lesion at the center of the region.