The increase of oral squamous cell carcinoma (OSCC) in many countries is primarily linked to its high mortality rate and poor forecast. The diagnosis of patients with OSCC is usually made by a pathologist who has utilized decades of useful data from tissue biopsy samples. Human error increases while trying to identify the cells via hand from photographs of microscope samples. Previous studies have applied convolutional neural networks (CNNs) with pre-trained models to detect oral diseases to improve accuracy. However, this approach outcomes in a high number of false positives and false negatives, which may lead to inaccurate diagnoses. To detect oral squamous cell carcinoma cancer from histopathological images, we developed the AlexNet deep learning model. We used image preprocessing methods to enhance the quality of histopathology images by utilizing bilateral filtering and color normalization via histogram image enhancement. Additionally, we utilized AlexNet, MobileNetV3, and InceptionV3 for feature extraction, as well as the XGBoost classifier. To emphasize the reliability of our findings, we have expanded on the evaluation metrics (accuracy, precision, recall, and F1 score) achieved by the proposed model, particularly the accuracy of 99%, precision of 98.5%, recall of 98.5%, and F1 score of 99%, highlighting the model’s strength in OSCC detection. These findings highlight the effectiveness of transfer learning in detecting medical images, providing significant advancements in the early detection of OSCC and making a valuable contribution to the field of oral oncology.

Highlights A CNN and HOG model performs better than predefined CNN architectures, such as the Inception and ResNet-50. The usage of ADF image filtering algorithms was the most suitable filtering algorithm for medical images. The QSVM classifier enhances the classification process's performance and is more effective than traditional SVMs.

Tuberculosis (TB) is a highly contagious disease that affects millions of individuals globally. Early detection is crucial in preventing its spread and improving patient outcomes. Radiologists often utilize X-ray imaging as a diagnostic tool for tuberculosis; however, the accuracy of the results may differ depending on the radiologist’s interpretation. To increase the precision of TB identification from X-ray images, a hybrid strategy combining Convolutional Neural Network, Histogram of Oriented Gradients, and Quantum Support Vector Machine (QSVM) classifier has been developed. Quantum machine learning is a rapidly growing field at the intersection of quantum computing and machine learning. By combining the strengths of both techniques, this approach aims to capture relevant features and shape and texture information of an image. The study used state-of-the-art models such as VGG16 and AlexNet, along with a handcrafted method (HOG) and a deep learning (CNN) method to detect TB from chest X-ray images. Six distinct tests were carried out by the authors to successfully diagnose tuberculosis. All models showed promise when the data was analyzed, but the best techniques involved augmentation, image preprocessing, contrast improvement, and noise filtering. The block-matching and 3D filtering approach was used to improve image edge preservation and reduce noise. In this study, Grad-CAM (gradient-weighted class activation map) was applied to the convolutional neural network model to identify the model’s important features (explainability) for decision-making. This focus on Explainable AI (XAI) is crucial for clinical adoption, as it provides radiologists with visual evidence of the model’s reasoning, fostering trust and enabling more informed decisions. The hybrid model was found to have achieved an exceptional level of accuracy in detecting and classifying TB, with an accuracy rate of 100% during training, 99.07% during validation, and 98.14% during testing. These results highlight the effectiveness of the hybrid model in accurately identifying TB and its potential to be a valuable tool in the fight against this disease.