AIMC Topic: Neural Networks, Computer

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K-Means Clustering and Classification of Breast Cancer Images Using Histogram of Oriented Gradients Features and Convolutional Neural Network Models: Diagnostic Image Analysis Study.

JMIR formative research
BACKGROUND: Breast cancer has proven to be the most common type of cancer among females around the world. However, mortality rates can be reduced if it is diagnosed at the initial stages. Interpretation made by an expert is required by conventional d...

Indigenous wood species classification using a multi-stage deep learning with grad-CAM explainability and an ensemble technique for Northern Bangladesh.

PloS one
Wood species recognition has recently emerged as a vital field in the realm of forestry and ecological conservation. Early studies in this domain have offered various methods for classifying distinct wood species found worldwide using data collected ...

Prediction of 1p/19q state in glioma by integrated deep learning method based on MRI radiomics.

BMC cancer
PURPOSE: To predict the 1p/19q molecular status of Lower-grade glioma (LGG) patients nondestructively, this study developed a deep learning (DL) approach using radiomic to provide a potential decision aid for clinical determination of molecular strat...

DBA-ViNet: an effective deep learning framework for fruit disease detection and classification using explainable AI.

BMC plant biology
OBJECTIVE: The primary aim of this research is to develop an effective and robust model for identifying and classifying diseases in general fruits, particularly apples, guavas, mangoes, pomegranates, and oranges, utilizing computer vision techniques.

Evaluating crop yield prediction models in illinois using aquacrop, semi-physical model and artificial neural networks.

Scientific reports
Crop yield is important for agricultural productivity and the country's economy. While crop yield estimation is an essential aspect of modern agriculture, it continues to be one of the most challenging tasks to manage effectively. Corn and soybean ar...

Quantitative evaluation of hydrocarbon contamination in soil using hyperspectral data-a comparative study of machine learning models.

Environmental monitoring and assessment
This study aims to evaluate the applicability of existing machine learning and deep learning techniques for the rapid prediction of hydrocarbon contamination in soils using hyperspectral data. Soil samples of three types, i.e., clayey, silty, and san...

Integration of pre-trained protein language models with equivariant graph neural networks for peptide toxicity prediction.

BMC biology
BACKGROUND: Peptide-based therapeutics have great potential due to their versatility, high specificity, and suitability for a variety of therapeutic applications. Despite these advantages, the inherent toxicities of some peptides pose challenges in d...

CVT-HNet: a fusion model for recognizing perianal fistulizing Crohn's disease based on CNN and ViT.

BMC medical imaging
BACKGROUND: Accurate identification of anal fistulas is essential, as it directly impacts the severity of subsequent perianal infections, prognostic indicators, and overall treatment outcomes. Traditional manual recognition methods are inefficient. I...

Creating interpretable deep learning models to identify species using environmental DNA sequences.

Scientific reports
Monitoring species' presence in an ecosystem is crucial for conservation and understanding habitat diversity, but can be expensive and time consuming. As a result, ecologists have begun using the DNA that animals naturally leave behind in water or so...

Harnessing infrared thermography and multi-convolutional neural networks for early breast cancer detection.

Scientific reports
Breast cancer is a relatively common carcinoma among women worldwide and remains a considerable public health concern. Consequently, the prompt identification of cancer is crucial, as research indicates that 96% of cancers are treatable if diagnosed ...