Multiview clustering with graph autoencoder for reconstructing histopathological images in oral cancer

Breast cancer is associated with the highest morbidity rates for cancer diagnoses in the world and has become a major public health issue. Early diagnosis can increase the chance of successful treatment and survival. However, it is a very challenging and time-consuming task that relies on the experience of pathologists. The automatic diagnosis of breast cancer by analyzing histopathological images plays a significant role for patients and their prognosis. However, traditional feature extraction methods can only extract some low-level features of images, and prior knowledge is necessary to select useful features, which can be greatly affected by humans. Deep learning techniques can extract high-level abstract features from images automatically. Therefore, we introduce it to analyze histopathological images of breast cancer via supervised and unsupervised deep convolutional neural networks. First, we adapted Inception_V3 and Inception_ResNet_V2 architectures to the binary and multi-class issues of breast cancer histopathological image classification by utilizing transfer learning techniques. Then, to overcome the influence from the imbalanced histopathological images in subclasses, we balanced the subclasses with Ductal Carcinoma as the baseline by turning images up and down, right and left, and rotating them counterclockwise by 90 and 180 degrees. Our experimental results of the supervised histopathological image classification of breast cancer and the comparison to the results from other studies demonstrate that Inception_V3 and Inception_ResNet_V2 based histopathological image classification of breast cancer is superior to the existing methods. Furthermore, these findings show that Inception_ResNet_V2 network is the best deep learning architecture so far for diagnosing breast cancers by analyzing histopathological images. Therefore, we used Inception_ResNet_V2 to extract features from breast cancer histopathological images to perform unsupervised analysis of the images. We also constructed a new autoencoder network to transform the features extracted by Inception_ResNet_V2 to a low dimensional space to do clustering analysis of the images. The experimental results demonstrate that using our proposed autoencoder network results in better clustering results than those based on features extracted only by Inception_ResNet_V2 network. All of our experimental results demonstrate that Inception_ResNet_V2 network based deep transfer learning provides a new means of performing analysis of histopathological images of breast cancer.

Overall prediction of oral cavity squamous cell carcinoma (OCSCC) remains inadequate, as more than half of patients with oral cavity cancer are detected at later stages. It is generally accepted that the differential diagnosis of OCSCC is usually difficult and requires expertise and experience. Diagnosis from biopsy tissue is a complex process, and it is slow, costly, and prone to human error. To overcome these problems, a computer-aided diagnosis (CAD) approach was proposed in this work. A dataset comprising two categories, normal epithelium of the oral cavity (NEOR) and squamous cell carcinoma of the oral cavity (OSCC), was used. Feature extraction was performed from this dataset using four deep learning (DL) models (VGG16, AlexNet, ResNet50, and Inception V3) to realize artificial intelligence of medial things (AIoMT). Binary Particle Swarm Optimization (BPSO) was used to select the best features. The effects of Reinhard stain normalization on performance were also investigated. After the best features were extracted and selected, they were classified using the XGBoost. The best classification accuracy of 96.3% was obtained when using Inception V3 with BPSO. This approach significantly contributes to improving the diagnostic efficiency of OCSCC patients using histopathological images while reducing diagnostic costs.

- Examining histopathology slides visually is a key technique utilized by pathologists to determine the stage, type, and subtype of lung tumors. The most common subtypes of lung cancer are adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC), and distinguishing between them necessitates careful visual evaluation by a skilled pathologist. The deep learning and machine learning techniques used to categorize histopathological lung cancer images are thoroughly examined in this work to aid in diagnosing lung cancer. Digital tissue pathology improves diagnosis accuracy and gives the pathologist more detail and better image quality with multiple viewing options and team annotations. Histopathological images are very useful for examining the conditions of different biological structures and diagnosing diseases like cancer. Key Words: Lung cancer, Machine Learning, Histopathological Image, Deep Learning

Breast cancer is a serious threat to women. Many machine learning-based computer-aided diagnosis (CAD) methods have been proposed for the early diagnosis of breast cancer based on histopathological images. Even though many such classification methods achieved high accuracy, many of them lack the explanation of the classification process. In this paper, we compare the performance of conventional machine learning (CML) against deep learning (DL)-based methods. We also provide a visual interpretation for the task of classifying breast cancer in histopathological images. For CML-based methods, we extract a set of handcrafted features using three feature extractors and fuse them to get image representation that would act as an input to train five classical classifiers. For DL-based methods, we adopt the transfer learning approach to the well-known VGG-19 deep learning architecture, where its pre-trained version on the large scale ImageNet, is block-wise fine-tuned on histopathological images. The evaluation of the proposed methods is carried out on the publicly available BreaKHis dataset for the magnification dependent classification of benign and malignant breast cancer and their eight sub-classes, and a further validation on KIMIA Path960, a magnification-free histopathological dataset with 20 image classes, is also performed. After providing the classification results of CML and DL methods, and to better explain the difference in the classification performance, we visualize the learned features. For the DL-based method, we intuitively visualize the areas of interest of the best fine-tuned deep neural networks using attention maps to explain the decision-making process and improve the clinical interpretability of the proposed models. The visual explanation can inherently improve the pathologist’s trust in automated DL methods as a credible and trustworthy support tool for breast cancer diagnosis. The achieved results show that DL methods outperform CML approaches where we reached an accuracy between 94.05% and 98.13% for the binary classification and between 76.77% and 88.95% for the eight-class classification, while for DL approaches, the accuracies range from 85.65% to 89.32% for the binary classification and from 63.55% to 69.69% for the eight-class classification.

doc-1 is a putative tumor suppressor gene isolated and identified from the hamster oral cancer model. Here, we report the molecular cloning and the functional characterization of the human ortholog of the hamster doc-1 gene. Human doc-1 cDNA is 1.6 kilobase pairs in length and encodes for a 115-amino acid polypeptide (12.4 kDa, pI 9. 53). Sequence analysis showed 98% identity between human and hamster doc-1 protein sequences. DOC-1 is expressed in all normal human tissues examined. In oral keratinocytes, expression of DOC-1 is restricted to normal oral keratinocytes. By immunostaining of normal human mucosa, DOC-1 is detected in both the cytoplasm and nuclei of basal oral keratinocytes; while in suprabasilar cells, it is primarily found in the nuclei. Human oral cancers in vivo did not exhibit immunostaining for DOC-1. Like murine DOC-1, human DOC-1 associates with DNA polymerase alpha/primase and mediates the phosphorylation of the large p180 catalytic subunit, suggesting it may be a potential regulator of DNA replication in the S phase of the cell cycle. Using a human doc-1 cosmid as a probe, human doc-1 is mapped to chromosome 12q24. We identified four exons in the entire human doc-1 gene and determined the intron-exon boundaries. By polymerase chain reaction and direct sequencing, we examined premalignant oral lesion and oral cancer cell lines and found no intragenic mutations.

Oral cancer is a dangerous disease which is mainly seen among men compared to women across the world. Oral cancer can cause death if it is not diagnosed early. Among the oral cancer types, oral squamous cell carcinoma is commonly seen. Detection of oral cancer is done using the histopathology images. Since histopathology images have different tissue and nucleus structures, feature extraction is always difficult. We propose a method for feature extraction and classification of oral histopathology images into normal or cancerous (Oral squamous cell carcinoma). In the proposed method, the normal and oral squamous cell carcinoma histopathology images are decomposed into one approximation and three detail coefficients by Haar wavelets. Features are then extracted from the high frequency and low-frequency coefficients with the Gray Level Co-occurrence Matrix. The features are fused to obtain a feature vector and the relevant features are selected by using an extra trees classifier. Finally, we used Random forest, K Nearest Neighbour and Voting classifier for the binary classification into normal and cancerous. The performance of the algorithm is evaluated using the performance metrics such as accuracy, precision, confusion matrix, sensitivity, specificity, F1 score, Quality index, error rate, ROC curves and AUC values of characteristic curves. Among the classifiers, Random forest gives high accuracy of 97.59% and a precision of 95.98%. The proposed method for detecting oral cancer is effective and precise. Hence, it can be used as a reliable and accurate support tool for oral pathologists.

Oral Squamous Cell Carcinoma (OSCC) causes a severe challenge in oncology due to the lack of diagnostic devices, leading to delays in detecting the disorder. The OSCC diagnosis through histopathology demands a pathologist expert because the cellular presentation is variable and highly complex. Existing diagnostic approaches for OSCC have specific efficiency and accuracy restrictions, highlighting the necessity for more reliable techniques. The increase of deep neural networks (DNN) model and their applications in medical imaging have been instrumental in disease diagnosis and detection. Automatic detection systems using deep learning (DL) approaches show tremendous promise in investigating medical imagery with speed, efficiency, and accuracy. In terms of OSCC, this system allows the diagnostic method to be streamlined, facilitating earlier diagnosis and enhancing survival rates. Automatic analysis of histopathological image (HI) can assist in accurately detecting and identifying tumorous tissue, reducing diagnostic turnaround times and increasing the efficacy of pathologists. This study presents a Squeeze-Excitation with Hybrid Deep Learning for Oral Squamous Cell Carcinoma Recognition (SEHDL-OSCCR) on HIs. The presented SEHDL-OSCCR technique mainly focuses on detecting oral cancer (OC) using hybrid DL models. The bilateral filtering (BF) technique is initially used to remove the noise. Next, the SEHDL-OSCCR technique employs the SE-CapsNet model to recognize the feature extractors. An improved crayfish optimization algorithm (ICOA) technique is utilized to improve the performance of the SE-CapsNet model. At last, the classification of the OSCC technique is performed by employing a convolutional neural network with a bidirectional long short-term memory (CNN-BiLSTM) model. The simulation results obtained using the SEHDL-OSCCR technique are investigated using a benchmark medical image dataset. The experimental validation of the SEHDL-OSCCR technique illustrated a greater accuracy outcome of 98.75% compared to recent approaches.

Gliomas are primary brain tumors caused by glial cells. These cancers’ classification and grading are crucial for prognosis and treatment planning. Deep learning (DL) can potentially improve the digital pathology investigation of brain tumors. In this paper, we developed a technique for visualizing a predictive tumor grading model on histopathology pictures to help guide doctors by emphasizing characteristics and heterogeneity in forecasts. The proposed technique is a hybrid model based on YOLOv5 and ResNet50. The function of YOLOv5 is to localize and classify the tumor in large histopathological whole slide images (WSIs). The suggested technique incorporates ResNet into the feature extraction of the YOLOv5 framework, and the detection results show that our hybrid network is effective for identifying brain tumors from histopathological images. Next, we estimate the glioma grades using the extreme gradient boosting classifier. The high-dimensional characteristics and nonlinear interactions present in histopathology images are well-handled by this classifier. DL techniques have been used in previous computer-aided diagnosis systems for brain tumor diagnosis. However, by combining the YOLOv5 and ResNet50 architectures into a hybrid model specifically designed for accurate tumor localization and predictive grading within histopathological WSIs, our study presents a new approach that advances the field. By utilizing the advantages of both models, this creative integration goes beyond traditional techniques to produce improved tumor localization accuracy and thorough feature extraction. Additionally, our method ensures stable training dynamics and strong model performance by integrating ResNet50 into the YOLOv5 framework, addressing concerns about gradient explosion. The proposed technique is tested using the cancer genome atlas dataset. During the experiments, our model outperforms the other standard ways on the same dataset. Our results indicate that the proposed hybrid model substantially impacts tumor subtype discrimination between low-grade glioma (LGG) II and LGG III. With 97.2% of accuracy, 97.8% of precision, 98.6% of sensitivity, and the Dice similarity coefficient of 97%, the proposed model performs well in classifying four grades. These results outperform current approaches for identifying LGG from high-grade glioma and provide competitive performance in classifying four categories of glioma in the literature.

Breast cancer is diagnosed using histopathological imaging. This task is extremely time-consuming due to high image complexity and volume. However, it is important to facilitate the early detection of breast cancer for medical intervention. Deep learning (DL) has become popular in medical imaging solutions and has demonstrated various levels of performance in diagnosing cancerous images. Nonetheless, achieving high precision while minimizing overfitting remains a significant challenge for classification solutions. The handling of imbalanced data and incorrect labeling is a further concern. Additional methods, such as pre-processing, ensemble, and normalization techniques, have been established to enhance image characteristics. These methods could influence classification solutions and be used to overcome overfitting and data balancing issues. Hence, developing a more sophisticated DL variant could improve classification accuracy while reducing overfitting. Technological advancements in DL have fueled automated breast cancer diagnosis growth in recent years. This paper reviewed studies on the capability of DL to classify histopathological breast cancer images, as the objective of this study was to systematically review and analyze current research on the classification of histopathological images. Additionally, literature from the Scopus and Web of Science (WOS) indexes was reviewed. This study assessed recent approaches for histopathological breast cancer image classification in DL applications for papers published up until November 2022. The findings of this study suggest that DL methods, especially convolution neural networks and their hybrids, are the most cutting-edge approaches currently in use. To find a new technique, it is necessary first to survey the landscape of existing DL approaches and their hybrid methods to conduct comparisons and case studies.

ObjectivesNow a days, squamous cell carcinoma (SCC) margin assessment is done by examining histopathology images and inspection of whole slide images (WSI) using a conventional microscope. This is time-consuming, tedious, and depends on experts’ experience which may lead to misdiagnosis and mistreatment plans. This study aims to develop a system for the automatic diagnosis of skin cancer margin for squamous cell carcinoma from histopathology microscopic images by applying deep learning techniques.MethodsThe system was trained, validated, and tested using histopathology images of SCC cancer locally acquired from Jimma Medical Center Pathology Department from seven different skin sites using an Olympus digital microscope. All images were preprocessed and trained with transfer learning pre-trained models by fine-tuning the hyper-parameter of the selected models.ResultsThe overall best training accuracy of the models become 95.3%, 97.1%, 89.8%, and 89.9% on EffecientNetB0, MobileNetv2, ResNet50, VGG16 respectively. In addition to this, the best validation accuracy of the models was 94.7%, 91.8%, 87.8%, and 86.7% respectively. The best testing accuracy of the models at the same epoch was 95.2%, 91.5%, 87%, and 85.5% respectively. From these models, EfficientNetB0 showed the best average training and testing accuracy than the other models.ConclusionsThe system assists the pathologist during the margin assessment of SCC by decreasing the diagnosis time from an average of 25 minutes to less than a minute.

Integrating local and global attention mechanisms for enhanced oral cancer detection and explainability.

Background Cases of Human papillomavirus (HPV)-associated oral and oropharyngeal cancer are increasing. Proper diagnostic tools are required to detect HPV among patients, especially in areas where high technology is lacking. Aims To provide mapping of HPV prevalence in Southeast Asia and to determine the effectivity of p16 as a surrogate biomarker for HPV infection in oral and oropharyngeal cancer. Methods Medical records of 56 patients diagnosed with oral and oropharyngeal squamous cell carcinomas (SCC) were reviewed. HPV PCR DNA and p16 immunohistochemistry (IHC) examination were performed to detect HPV positivity. Results HPV PCR prevalence in oropharyngeal SCC is 42.9% and 28.6% in oral SCC. P16 IHC has 67% sensitivity and 75% specificity in detecting HPV in oropharyngeal cancer, and 33% and 72% in oral cancer. Conclusion We conclude that p16 IHC with a 5% cut-off can be used as a surrogate biomarker for oropharyngeal SCC, but not oral SCC, in areas where resources are restricted. However, further diagnostic tools may be needed.

Earlier detection and classification of squamous cell carcinoma (OSCC) is a widespread issue for efficient treatment, enhancing survival rate, and reducing the death rate. Thus, it becomes necessary to design effective diagnosis models for assisting pathologists in the OSCC examination process. In recent times, deep learning (DL) models have exhibited considerable improvement in the design of effective computer-aided diagnosis models for OSCC using histopathological images. In this view, this paper develops a novel duck pack optimization with deep transfer learning enabled oral squamous cell carcinoma classification (DPODTL-OSC3) model using histopathological images. The goal of the DPODTL-OSC3 model is to improve the classifier outcomes of OSCC using histopathological images into normal and cancerous class labels. Finally, the variational autoencoder (VAE) model is utilized for the detection and classification of OSCC. The performance validation and comparative result analysis for the DPODTL-OSC3 model are tested using a histopathological imaging database.

Humankind is witnessing a gradual increase in cancer incidence, emphasizing the importance of early diagnosis and treatment, and follow-up clinical protocols. Oral or mouth cancer, categorized under head and neck cancers, requires effective screening for timely detection. This study proposes a framework, OralNet, for oral cancer detection using histopathology images. The research encompasses four stages: (i) Image collection and preprocessing, gathering and preparing histopathology images for analysis; (ii) feature extraction using deep and handcrafted scheme, extracting relevant features from images using deep learning techniques and traditional methods; (iii) feature reduction artificial hummingbird algorithm (AHA) and concatenation: Reducing feature dimensionality using AHA and concatenating them serially and (iv) binary classification and performance validation with three-fold cross-validation: Classifying images as healthy or oral squamous cell carcinoma and evaluating the framework's performance using three-fold cross-validation. The current study examined whole slide biopsy images at 100× and 400× magnifications. To establish OralNet's validity, 3000 cropped and resized images were reviewed, comprising 1500 healthy and 1500 oral squamous cell carcinoma images. Experimental results using OralNet achieved an oral cancer detection accuracy exceeding 99.5%. These findings confirm the clinical significance of the proposed technique in detecting oral cancer presence in histology slides.

ABSTRACTIntroduction: As many as 90%-95% cases of oral cancer are oral squamous cell carcinoma. In South and Southeast Asia, the prevalence of oral cancer is high. Oral cancer is ranked sixth for the most frequent malignancies in Asia with nearly 274,300 new cases occurring every year. Head and Neck Squamous Cell Carcinoma is one of the main public health concerns with 1-2% incident of all cancers worldwide and is also the sixth most common malignant tumor. Human Papillomavirus (HPV) is a DNA virus which is also known as the cause of head and neck cancer. Among all malignancies, oral cancer is one of the conditions where salivary examination can be used to detect oral cancer because it contacts directly with the lesions. The relationship between human papilloma virus (HPV) and oral squamous cell carcinoma has been extensively studied and shows that HPV-16 & 18 can be detected in saliva, serum or plasma samples and patient biopsies. Purpose: This systematic review aims to reveal the presence of human papilloma virus in cancerous and pre-cancerous lesions of the oral cavity by examining saliva, serum or plasma. Methods: Systematic Review was carried out using the PRISMA method. Articles with clinical trial types sourced from electronic searches were obtained through the PubMed, Research Gate and Google Scholar portals using keywords: Human Papilloma Virus, Oral Cancer, and Saliva. Conclusion: Based on the results, it is concluded that HPV is detected in oral cavity cancers, oropharyngeal, oral squamous cell carcinoma and Oral Potentially Malignant Disorders.Keywords : Human Papilloma Virus, Oral cancer, Saliva