AIMC Topic: Diagnosis, Computer-Assisted

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An Improved MobileNet Network with Wavelet Energy and Global Average Pooling for Rotating Machinery Fault Diagnosis.

Sensors (Basel, Switzerland)
In recent years, neural networks have shown good performance in terms of accuracy and efficiency. However, along with the continuous improvement in diagnostic accuracy, the number of parameters in the network is increasing and the models can often on...

A metric learning-based method using graph neural network for pancreatic cystic neoplasm classification from CTs.

Medical physics
PURPOSE: Pancreatic cystic neoplasms (PCNs) are relatively rare neoplasms and difficult to be classified preoperatively. Ordinary deep learning methods have great potential to provide support for doctors in PCNs classification but require a quantity ...

ThoraciNet: thoracic abnormality detection and disease classification using fusion DCNNs.

Physical and engineering sciences in medicine
Chest X-rays are arguably the de facto medical imaging technique for diagnosing thoracic abnormalities. Chest X-ray analysis is complex, especially in asymptomatic diseases, and relies heavily on the expertise of radiologists. This work proposes the ...

Feature discretization-based deep clustering for thyroid ultrasound image feature extraction.

Computers in biology and medicine
Ultrasound imaging technology has the advantage of being convenient, less harmful and widely applied, making ultrasonography one of the most popular methods for disease diagnosis. With the rapid development of Computer- Aided Diagnosis (CAD) technolo...

YOLO-LOGO: A transformer-based YOLO segmentation model for breast mass detection and segmentation in digital mammograms.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Both mass detection and segmentation in digital mammograms play a crucial role in early breast cancer detection and treatment. Furthermore, clinical experience has shown that they are the upstream tasks of pathological class...

Automatic recognition of micronucleus by combining attention mechanism and AlexNet.

BMC medical informatics and decision making
BACKGROUND: Micronucleus (MN) is an abnormal fragment in a human cell caused by disorders in the mechanism regulating chromosome segregation. It can be used as a biomarker for genotoxicity, tumor risk, and tumor malignancy. The in vitro micronucleus ...

A computer-aided diagnosis system for detecting various diabetic retinopathy grades based on a hybrid deep learning technique.

Medical & biological engineering & computing
Diabetic retinopathy (DR) is a serious disease that may cause vision loss unawares without any alarm. Therefore, it is essential to scan and audit the DR progress continuously. In this respect, deep learning techniques achieved great success in medic...

An improved CNN-based architecture for automatic lung nodule classification.

Medical & biological engineering & computing
Lung cancer is one of the most critical diseases due to its significant death rate compared to all other types of cancer. The early diagnosis of lung cancer that improves the patient's chance of surviving is mostly done in two phases: screening throu...

Recent advancement in cancer diagnosis using machine learning and deep learning techniques: A comprehensive review.

Computers in biology and medicine
Being a second most cause of mortality worldwide, cancer has been identified as a perilous disease for human beings, where advance stage diagnosis may not help much in safeguarding patients from mortality. Thus, efforts to provide a sustainable archi...

Tooth detection for each tooth type by application of faster R-CNNs to divided analysis areas of dental panoramic X-ray images.

Radiological physics and technology
This study aimed to propose a computerized method for detecting the tooth region for each tooth type as the initial stage in the development of a computer-aided diagnosis (CAD) scheme for dental panoramic X-ray images. Our database consists of 160 pa...