The transition module: a method for preventing overfitting in convolutional neural networks

Machine Learning Allows for Distinguishing Precancerous and Cancerous Human Epithelial Cervical Cells Using High-Resolution AFM Imaging of Adhesion Maps

Previously, the analysis of atomic force microscopy (AFM) images allowed us to distinguish normal from cancerous/precancerous human epithelial cervical cells using only the fractal dimension parameter. High-resolution maps of adhesion between the AFM probe and the cell surface were used in that study. However, the separation of cancerous and precancerous cells was rather poor (the area under the curve (AUC) was only 0.79, whereas the accuracy, sensitivity, and specificity were 74%, 58%, and 84%, respectively). At the same time, the separation between premalignant and malignant cells is the most significant from a clinical point of view. Here, we show that the introduction of machine learning methods for the analysis of adhesion maps allows us to distinguish precancerous and cancerous cervical cells with rather good precision (AUC, accuracy, sensitivity, and specificity are 0.93, 83%, 92%, and 78%, respectively). Substantial improvement in sensitivity is significant because of the unmet need in clinical practice to improve the screening of cervical cancer (a relatively low specificity can be compensated by combining this approach with other currently existing screening methods). The random forest decision tree algorithm was utilized in this study. The analysis was carried out using the data of six precancerous primary cell lines and six cancerous primary cell lines, each derived from different humans. The robustness of the classification was verified using K-fold cross-validation (K = 500). The results are statistically significant at p < 0.0001. Statistical significance was determined using the random shuffle method as a control.

Radiomics Applications in Head and Neck Tumor Imaging: A Narrative Review

Recent advances in machine learning and artificial intelligence technology have ensured automated evaluation of medical images. As a result, quantifiable diagnostic and prognostic biomarkers have been created. We discuss radiomics applications for the head and neck region in this paper. Molecular characterization, categorization, prognosis and therapy recommendation are given special consideration. In a narrative manner, we outline the fundamental technological principles, the overall idea and usual workflow of radiomic analysis and what seem to be the present and potential challenges in normal clinical practice. Clinical oncology intends for all of this to ensure informed decision support for personalized and useful cancer treatment. Head and neck cancers present a unique set of diagnostic and therapeutic challenges. These challenges are brought on by the complicated anatomy and heterogeneity of the area under investigation. Radiomics has the potential to address these barriers. Future research must be interdisciplinary and focus on the study of certain oncologic functions and outcomes, with external validation and multi-institutional cooperation in order to achieve this.

Intelligent Ultra-Light Deep Learning Model for Multi-Class Brain Tumor Detection

The diagnosis and surgical resection using Magnetic Resonance (MR) images in brain tumors is a challenging task to minimize the neurological defects after surgery owing to the non-linear nature of the size, shape, and textural variation. Radiologists, clinical experts, and brain surgeons examine brain MRI scans using the available methods, which are tedious, error-prone, time-consuming, and still exhibit positional accuracy up to 2–3 mm, which is very high in the case of brain cells. In this context, we propose an automated Ultra-Light Brain Tumor Detection (UL-BTD) system based on a novel Ultra-Light Deep Learning Architecture (UL-DLA) for deep features, integrated with highly distinctive textural features, extracted by Gray Level Co-occurrence Matrix (GLCM). It forms a Hybrid Feature Space (HFS), which is used for tumor detection using Support Vector Machine (SVM), culminating in high prediction accuracy and optimum false negatives with limited network size to fit within the average GPU resources of a modern PC system. The objective of this study is to categorize multi-class publicly available MRI brain tumor datasets with a minimum time thus real-time tumor detection can be carried out without compromising accuracy. Our proposed framework includes a sensitivity analysis of image size, One-versus-All and One-versus-One coding schemes with stringent efforts to assess the complexity and reliability performance of the proposed system with K-fold cross-validation as a part of the evaluation protocol. The best generalization achieved using SVM has an average detection rate of 99.23% (99.18%, 98.86%, and 99.67%), and F-measure of 0.99 (0.99, 0.98, and 0.99) for (glioma, meningioma, and pituitary tumors), respectively. Our results have been found to improve the state-of-the-art (97.30%) by 2%, indicating that the system exhibits capability for translation in modern hospitals during real-time surgical brain applications. The method needs 11.69 ms with an accuracy of 99.23% compared to 15 ms achieved by the state-of-the-art to earlier to detect tumors on a test image without any dedicated hardware providing a route for a desktop application in brain surgery.

Recent Applications of Artificial Intelligence in detection of Gastrointestinal, Hepatic and Pancreatic Diseases

There is substantial progress in artificial intelligence (AI) algorithms and their medical sciences applications in the last two decades. AI-assisted programs have already been established for remotely health monitoring using sensors and smartphones. A variety of AI-based prediction models available for the gastrointestinal inflammatory, non-malignant diseases, and bowel bleeding using wireless capsule endoscopy, electronic medical records for hepatitis-associated fibrosis, pancreatic carcinoma using endoscopic ultrasounds. AI-based models may be of immense help for healthcare professionals in the identification, analysis, and decision support using endoscopic images to establish prognosis and risk assessment of patient's treatment using multiple factors. Although enough randomized clinical trials are warranted to establish the efficacy of AI-algorithms assisted and non-AI based treatments before approval of such techniques from medical regulatory authorities. In this article, available AI approaches and AI-based prediction models for detecting gastrointestinal, hepatic, and pancreatic diseases are reviewed. The limitation of AI techniques in such disease prognosis, risk assessment, and decision support are discussed.

DeepSnap-Deep Learning Approach Predicts Progesterone Receptor Antagonist Activity With High Performance

The progesterone receptor (PR) is important therapeutic target for many malignancies and endocrine disorders due to its role in controlling ovulation and pregnancy via the reproductive cycle. Therefore, the modulation of PR activity using its agonists and antagonists is receiving increasing interest as novel treatment strategy. However, clinical trials using the PR modulators have not yet been found conclusive evidences. Recently, increasing evidence from several fields shows that the classification of chemical compounds, including agonists and antagonists, can be done with recent improvements in deep learning (DL) using deep neural network. Therefore, we recently proposed a novel DL-based quantitative structure-activity relationship (QSAR) strategy using transfer learning to build prediction models for agonists and antagonists. By employing this novel approach, referred as DeepSnap-DL method, which uses images captured from 3-dimension (3D) chemical structure with multiple angles as input data into the DL classification, we constructed prediction models of the PR antagonists in this study. Here, the DeepSnap-DL method showed a high performance prediction of the PR antagonists by optimization of some parameters and image adjustment from 3D-structures. Furthermore, comparison of the prediction models from this approach with conventional machine learnings (MLs) indicated the DeepSnap-DL method outperformed these MLs. Therefore, the models predicted by DeepSnap-DL would be powerful tool for not only QSAR field in predicting physiological and agonist/antagonist activities, toxicity, and molecular bindings; but also for identifying biological or pathological phenomena.