This study introduces a sophisticated neural network structure for segmenting breast tumors.
It achieves this by combining a pretrained Vision Transformer (ViT) model with a UNet …

This study utilizes the Breast Ultrasound Image (BUSI) dataset to present a deep learning
technique for breast tumor segmentation based on a modified UNet architecture. To improve …

Polyp segmentation is essential for the early detection and treatment of colorectal cancer
using colonoscopy images. The computer-aided diagnosis (CAD) systems assist …

Diverse breast masses in size, shape and place make accurate image segmentation more
challenging in a unified deep-learning network. Therefore, based on the U-net network, an …

This study presents a novel approach for brain MRI classification by integrating multiple
state-of-the-art deep learning (DL) architectures, including VGG16, EfficientNet, MobileNet …

Medical image segmentation through the use of deep learning is becoming a trend targeting
to automate disease detection and provide effective treatment. Traditionally huge manual …

In an era where customer satisfaction and loyalty are pivotal for business success,
understanding the myriad factors influencing consumer behavior presents a significant …

This study evaluates various machine learning models for classifying prostate cancer using
gene expression profiles from DNA microarrays. Due to the high dimensionality of these …

Breast cancer is the second most common cause of cancer-related death in women and one
of the deadliest diseases. Malignant, carcinogenic tumors that develop from breast cells are …

Data processing methods based on neural networks are becoming increasingly popular in
medical diagnostics. They are most commonly used to evaluate medical images of human …