AIMC Topic: Image Processing, Computer-Assisted

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Enhanced melanoma and non-melanoma skin cancer classification using a hybrid LSTM-CNN model.

Scientific reports
Melanoma is the most dangerous type of skin cancer. Although it accounts for only about 1% of all skin cancer cases, it is responsible for the majority of skin cancer-related deaths. Early detection and accurate diagnosis are crucial for improving th...

Leveraging machine learning techniques for image classification and revealing social media insights into human engagement with urban wild spaces.

Scientific reports
In recent years, machine learning models have exhibited excellent performance and far-reaching impact across domains such as fraud detection in finance, recommendation systems in e-commerce, medical imaging in healthcare, agricultural forecasting, so...

Deformable detection transformers for domain adaptable ultrasound localization microscopy with robustness to point spread function variations.

Scientific reports
Super-resolution imaging has emerged as a rapidly advancing field in diagnostic ultrasound. Ultrasound Localization Microscopy (ULM) achieves sub-wavelength precision in microvasculature imaging by tracking gas microbubbles (MBs) flowing through bloo...

Autoimmune gastritis detection from preprocessed endoscopy images using deep transfer learning and moth flame optimization.

Scientific reports
Gastric Tract Disease (GTD) constitutes a medical emergency, emphasizing the critical importance of early diagnosis and intervention to lessen its severity. Clinical practices often utilize endoscopy-supported examinations for GTD screening. The imag...

In-silico CT simulations of deep learning generated heterogeneous phantoms.

Biomedical physics & engineering express
Current virtual imaging phantoms primarily emphasize geometric accuracy of anatomical structures. However, to enhance realism, it is also important to incorporate intra-organ detail. Because biological tissues are heterogeneous in composition, virtua...

Nuclei segmentation and classification from histopathology images using federated learning for end-edge platform.

PloS one
Accurate nuclei segmentation and classification in histology images are critical for cancer detection but remain challenging due to color inconsistency, blurry boundaries, and overlapping nuclei. Manual segmentation is time-consuming and labor-intens...

Mitosis detection in histopathological images using customized deep learning and hybrid optimization algorithms.

PloS one
Identifying mitosis is crucial for cancer diagnosis, but accurate detection remains difficult because of class imbalance and complex morphological variations in histopathological images. To overcome this challenge, we propose a Customized Deep Learni...

Metaverse-based deep learning framework for coronary artery stenosis classification using Monte Carlo Dropout-based ResNet-152.

Computers in biology and medicine
Metaverse offers an immersive healthcare platform that combines virtual reality (VR) and artificial intelligence (AI), providing a new approach to medical diagnostics. However, difficulties such as inadequate spatial resolution, uncertainty managemen...

Development of a deep learning-based MRI diagnostic model for human Brucella spondylitis.

Biomedical engineering online
INTRODUCTION: Brucella spondylitis (BS) and tuberculous spondylitis (TS) are prevalent spinal infections with distinct treatment protocols. Rapid and accurate differentiation between these two conditions is crucial for effective clinical management; ...

Deep learning for predicting myopia severity classification method.

Biomedical engineering online
BACKGROUND: Myopia is a major cause of vision impairment. To improve the efficiency of myopia screening, this paper proposes a deep learning model, X-ENet, which combines the advantages of depthwise separable convolution and dynamic convolution to cl...