Breast mass identification is a crucial procedure during mammogram-based early breast cancer diagnosis. However, it is difficult to determine whether a breast lump is benign or cancerous at early stages. Convolutional neural networks (CNNs) have been used to solve this problem and have provided useful advancements. However, CNNs focus only on a certain portion of the mammogram while ignoring the remaining and present computational complexity because of multiple convolutions. Recently, vision transformers have been developed as a technique to overcome such limitations of CNNs, ensuring better or comparable performance in natural image classification. However, the utility of this technique has not been thoroughly investigated in the medical image domain. In this study, we developed a transfer learning technique based on vision transformers to classify breast mass mammograms. The area under the receiver operating curve of the new model was estimated as 1 ± 0, thus outperforming the CNN-based transfer-learning models and vision transformer models trained from scratch. The technique can, hence, be applied in a clinical setting, to improve the early diagnosis of breast cancer.

This study presents an ear-mounted photoplethysmography (PPG) system that is designed to detect mental stress. Mental stress is a prevalent condition that can negatively impact an individual’s health and well-being. Early detection and treatment of mental stress are crucial for preventing related illnesses and maintaining overall wellness. The study used data from 14 participants that were collected in a controlled environment. The participants were subjected to stress-inducing tasks such as the Stroop color–word test and mathematical calculations. The raw PPG signal was then preprocessed and transformed into scalograms using continuous wavelet transform (CWT). A convolutional neural network classifier was then used to classify the transformed signals as stressed or non-stressed. The results of the study show that the PPG system achieved high levels of accuracy (92.04%) and F1-score (90.8%). Furthermore, by adding white Gaussian noise to the raw PPG signals, the results were improved even more, with an accuracy of 96.02% and an F1-score of 95.24%. The proposed ear-mounted device shows great promise as a reliable tool for the early detection and treatment of mental stress, potentially revolutionizing the field of mental health and well-being.

Endoscopic pathological findings of the gastrointestinal tract are crucial for the early diagnosis of colorectal cancer (CRC). Previous deep learning works, aimed at improving CRC detection performance and reducing subjective analysis errors, are limited to polyp segmentation. Pathological findings were not considered and only convolutional neural networks (CNNs), which are not able to handle global image feature information, were utilized. This work introduces a novel vision transformer (ViT)-based approach for early CRC detection. The core components of the proposed approach are ViTCol, a boosted vision transformer for classifying endoscopic pathological findings, and PUTS, a vision transformer-based model for polyp segmentation. Results demonstrate the superiority of this vision transformer-based CRC detection method over existing CNN and vision transformer models. ViTCol exhibited an outstanding performance in classifying pathological findings, with an area under the receiver operating curve (AUC) value of 0.9999 ± 0.001 on the Kvasir dataset. PUTS provided outstanding results in segmenting polyp images, with mean intersection over union (mIoU) of 0.8673 and 0.9092 on the Kvasir-SEG and CVC-Clinic datasets, respectively. This work underscores the value of spatial transformers in localizing input images, which can seamlessly integrate into the main vision transformer network, enhancing the automated identification of critical image features for early CRC detection.

This study addresses the critical need for effective mental stress monitoring, linked to severe health issues like depression and heart disease. We introduce a robust method using in-ear photoplethysmography (PPG) signals for detecting and classifying stress levels. The objective of this study is to develop a precise stress monitoring technique using advanced signal processing and deep learning. Raw PPG data were collected from 15 subjects undergoing stress-inducing activities in a controlled setting. The data underwent preprocessing and were transformed into image-like time-frequency representations. We employed Vision Transformer (ViT) models for classification, which were fine-tuned and compared against other state-of-the-art deep learning models. The ViT classifier significantly outperformed existing models, achieving an average accuracy of 97.78% and an F1-score of 97.79%. While the dataset is relatively small, these results suggest a promising direction for stress monitoring by illustrating the potential of combining in-ear PPG signals with ViT models. The study indicates the efficacy of this novel approach for accurate mental stress diagnosis, which could have significant implications for mental health applications. Future work will focus on validating these findings with a larger sample size and exploring the integration of this technology into wearable devices for real-world stress monitoring.

Endoscopy has long been used as a gold standard for examination and diagnosis in the gastrointestinal (GI) tract. A large volume of images is typically captured during endoscopy, particularly when a video is recorded. These images take a significant amount of endoscopist's time to review and assess. Moreover, the review process is tedious and requires substantial experience. In this paper, we propose a superpixel-based technique for segmenting and classifying GI landmarks and diseases in endoscopic images. We split each image into several superpixels and extract texture features of local binary patterns, gray-level co-occurrence matrices, and first-order statistical features. These features are extracted from grayscale and color channels of different color spaces. Different combinations of the proposed multichannel superpixel features are used to train and test support vector machines. The performance of the superpixel-based method was evaluated and compared against a pixel-based method using the area under the receiver operating characteristic curve (AUC). Our method outperformed the pixel-based one with AUC values of 82.06%, 99.6%, and 97.4% in the classification of the upper, middle, and lower GI parts, respectively. In particular, for the upper GI part, our method resulted in an AUC improvement of 4.52% compared to the pixel-based method.

Automated emotion identification via physiological data from wearable devices is a growing field, yet traditional electroencephalography (EEG) and photoplethysmography (PPG) collection methods can be uncomfortable. This research introduces a novel structure of the in-ear wearable device that captures both PPG and EEG signals to enhance user comfort for emotion recognition. Data were collected from 21 individuals experiencing four emotional states (fear, happy, calm, sad) induced by video stimuli. Following signal preprocessing, temporal and frequency domain features were extracted and selected using the ReliefF approach. Classification accuracy was assessed for PPG, EEG, and combined features, with combined features yielding superior results. An XGBoost classifier, optimized with Bayesian hyperparameter tuning, achieved 97.58% accuracy, 97.57% precision, 97.57% recall, and a 97.58% F1 score, outperforming support vector machine, decision tree, random forest, and K-Nearest Neighbor classifiers. These findings highlight the benefits of multimodal physiological sensing and optimized machine learning for reliable emotion characterization, with implications for mental health monitoring and human-computer interaction.