Deep Learning-Based Classification of Macrofungi: Comparative Analysis of Advanced Models for Accurate Fungi Identification.

This study focuses on the classification of six different macrofungi species using advanced deep learning techniques. Fungi species, such as , , , , and were chosen based on their ecological importance and distinct morphological characteristics. The research employed 5 different machine learning techniques and 12 deep learning models, including DenseNet121, MobileNetV2, ConvNeXt, EfficientNet, and swin transformers, to evaluate their performance in identifying fungi from images.

Categorization of Alzheimer's disease stages using deep learning approaches with McNemar's test.

Early diagnosis is crucial in Alzheimer's disease both clinically and for preventing the rapid progression of the disease. Early diagnosis with awareness studies of the disease is of great importance in terms of controlling the disease at an early stage. Additionally, early detection can reduce treatment costs associated with the disease.

Cloud type classification using deep learning with cloud images.

Clouds play a pivotal role in determining the weather, impacting the daily lives of everyone. The cloud type can offer insights into whether the weather will be sunny or rainy and even serve as a warning for severe and stormy conditions. Classified into ten distinct classes, clouds provide valuable information about both typical and exceptional weather patterns, whether they are short or long-term in nature.

Enhancing Tissue Equivalence in Li Heavy Ion Therapy with MC Algorithm Optimized Polymer-Based Bioinks.

The unique physical properties of heavy ion beams, particularly their distinctive depth-dose distribution and sharp lateral dose reduction profiles, have led to their widespread adoption in tumor therapy worldwide. However, the physical properties of heavy ion beams must be investigated to deliver a sufficient dose to tumors without damaging organs at risk. These studies should be performed on phantoms made of biomaterials that closely mimic human tissue.

MC TRIM Algorithm in Mandibula Phantom in Helium Therapy.

Helium ion beam therapy, one of the particle therapies developed and studied in the 1950s for cancer treatment, resulted in clinical trials starting at Lawrence Berkeley National Laboratory in 1975. While proton and carbon ion therapies have been implemented in research institutions and hospitals globally after the end of the trials, progress in comprehending the physical, biological, and clinical findings of helium ion beam therapy has been limited, particularly due to its limited accessibility. Ongoing efforts aim to establish programs that evaluate the use of helium ion beams for clinical and research purposes, especially in the treatment of sensitive clinical cases.

Crohn's Disease Prediction Using Sequence Based Machine Learning Analysis of Human Microbiome.

Human microbiota refers to the trillions of microorganisms that inhabit our bodies and have been discovered to have a substantial impact on human health and disease. By sampling the microbiota, it is possible to generate massive quantities of data for analysis using Machine Learning algorithms. In this study, we employed several modern Machine Learning techniques to predict Inflammatory Bowel Disease using raw sequence data.

Deep Learning in Diagnosis of Dental Anomalies and Diseases: A Systematic Review.

Deep learning and diagnostic applications in oral and dental health have received significant attention recently. In this review, studies applying deep learning to diagnose anomalies and diseases in dental image material were systematically compiled, and their datasets, methodologies, test processes, explainable artificial intelligence methods, and findings were analyzed. Tests and results in studies involving human-artificial intelligence comparisons are discussed in detail to draw attention to the clinical importance of deep learning.

Monte Carlo Simulation of TRIM Algorithm in Ceramic Biomaterial in Proton Therapy.

Biomaterials play a crucial role in enhancing human health and quality of life. They are employed in applications such as tissue substitution, diagnostic tools, medical supplies, therapeutic treatments, regenerative medicine, and radiation dosimetric studies. However, their predisposition to proton therapy, which is a trending treatment in the world, has not been adequately studied.

Machine Learning Analysis of RNA-seq Data for Diagnostic and Prognostic Prediction of Colon Cancer.

Data from omics studies have been used for prediction and classification of various diseases in biomedical and bioinformatics research. In recent years, Machine Learning (ML) algorithms have been used in many different fields related to healthcare systems, especially for disease prediction and classification tasks. Integration of molecular omics data with ML algorithms has offered a great opportunity to evaluate clinical data.

Machine learning based orthodontic treatment planning for mixed dentition borderline cases suffering from moderate to severe crowding: An experimental research study.

Background: Pedodontists and general practitioners may need support in planning the early orthodontic treatment of patients with mixed dentition, especially in borderline cases. The use of machine learning algorithms is required to be able to consistently make treatment decisions for such cases.

Objective: This study aimed to use machine learning algorithms to facilitate the process of deciding whether to choose serial extraction or expansion of maxillary and mandibular dental arches for early treatment of borderline patients suffering from moderate to severe crowding.

A New Approach for Gastrointestinal Tract Findings Detection and Classification: Deep Learning-Based Hybrid Stacking Ensemble Models.

Endoscopic procedures for diagnosing gastrointestinal tract findings depend on specialist experience and inter-observer variability. This variability can cause minor lesions to be missed and prevent early diagnosis. In this study, deep learning-based hybrid stacking ensemble modeling has been proposed for detecting and classifying gastrointestinal system findings, aiming at early diagnosis with high accuracy and sensitive measurements and saving workload to help the specialist and objectivity in endoscopic diagnosis.

Femoral neck fracture detection in X-ray images using deep learning and genetic algorithm approaches.

Objectives: This study aims to detect frontal pelvic radiograph femoral neck fracture using deep learning techniques.

Patients And Methods: This retrospective study was conducted between January 2013 and January 2018. A total of 234 frontal pelvic X-ray images collected from 65 patients (32 males, 33 females; mean age 74.

Retrieving relevant experiments: The case of microRNA microarrays.

Content-based retrieval of biological experiments in large public repositories is a recent challenge in computational biology and bioinformatics. The task is, in general, to search in a database using a query-by-example without any experimental meta-data annotation. Here, we consider a more specific problem that seeks a solution for retrieving relevant microRNA experiments from microarray repositories.