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Automated detection and recognition system for chewable food items using advanced deep learning models
Identifying and recognizing the food on the basis of its eating sounds is a challenging task, as it plays an important role in avoiding allergic...
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Accuracy and data efficiency in deep learning models of protein expression
Synthetic biology often involves engineering microbial strains to express high-value proteins. Thanks to progress in rapid DNA synthesis and...
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Effects of MRI scanner manufacturers in classification tasks with deep learning models
Deep learning has become a leading subset of machine learning and has been successfully employed in diverse areas, ranging from natural language...
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Optimizing vitiligo diagnosis with ResNet and Swin transformer deep learning models: a study on performance and interpretability
Vitiligo is a hypopigmented skin disease characterized by the loss of melanin. The progressive nature and widespread incidence of vitiligo...
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Detecting common coccinellids found in sorghum using deep learning models
Increased global production of sorghum has the potential to meet many of the demands of a growing human population. Develo** automation...
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Detection and classification of brain tumor using hybrid deep learning models
Accurately classifying brain tumor types is critical for timely diagnosis and potentially saving lives. Magnetic Resonance Imaging (MRI) is a widely...
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Aphid cluster recognition and detection in the wild using deep learning models
Aphid infestation poses a significant threat to crop production, rural communities, and global food security. While chemical pest control is crucial...
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“KAIZEN” method realizing implementation of deep-learning models for COVID-19 CT diagnosis in real world hospitals
Numerous COVID-19 diagnostic imaging Artificial Intelligence (AI) studies exist. However, none of their models were of potential clinical use,...
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Clinical relevance of deep learning models in predicting the onset timing of cancer pain exacerbation
Cancer pain is a challenging clinical problem that is encountered in the management of cancer pain. We aimed to investigate the clinical relevance of...
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A Spitzoid Tumor dataset with clinical metadata and Whole Slide Images for Deep Learning models
Spitzoid tumors (ST) are a group of melanocytic tumors of high diagnostic complexity. Since 1948, when Sophie Spitz first described them, the...
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OCT-based deep-learning models for the identification of retinal key signs
A new system based on binary Deep Learning (DL) convolutional neural networks has been developed to recognize specific retinal abnormality signs on...
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Enhancing cervical cancer detection and robust classification through a fusion of deep learning models
Cervical cancer, the second most prevalent cancer affecting women, arises from abnormal cell growth in the cervix, a crucial anatomical structure...
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Employing deep learning and transfer learning for accurate brain tumor detection
Artificial intelligence-powered deep learning methods are being used to diagnose brain tumors with high accuracy, owing to their ability to process...
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Prediction of CO2 solubility in Ionic liquids for CO2 capture using deep learning models
Ionic liquids (ILs) are highly effective for capturing carbon dioxide (CO 2 ). The prediction of CO 2 solubility in ILs is crucial for optimizing CO 2 ...
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Comparison of different machine learning classification models for predicting deep vein thrombosis in lower extremity fractures
Deep vein thrombosis (DVT) is a common complication in patients with lower extremity fractures. Once it occurs, it will seriously affect the quality...
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Deep learning models for predicting the survival of patients with medulloblastoma based on a surveillance, epidemiology, and end results analysis
Medulloblastoma is a malignant neuroepithelial tumor of the central nervous system. Accurate prediction of prognosis is essential for therapeutic...
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Deep kernel learning of dynamical models from high-dimensional noisy data
This work proposes a stochastic variational deep kernel learning method for the data-driven discovery of low-dimensional dynamical models from...
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An automated framework for evaluation of deep learning models for splice site predictions
A novel framework for the automated evaluation of various deep learning-based splice site detectors is presented. The framework eliminates...
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Unsupervised anomaly detection for earthquake detection on Korea high-speed trains using autoencoder-based deep learning models
We propose a method for detecting earthquakes for high-speed trains based on unsupervised anomaly-detection techniques. In particular, we utilized...
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Annotated dataset for training deep learning models to detect astrocytes in human brain tissue
Astrocytes, a type of glial cell, significantly influence neuronal function, with variations in morphology and density linked to neurological...
