Skin cancer is recognized as one of the most harmful cancers worldwide. Early detection of this cancer is an effective measure for treating the disease efficiently. Traditional skin cancer detection methods face scalability challenges and overfitting...
The development of deep learning algorithms has transformed medical image analysis, especially in brain tumor recognition. This research introduces a robust automatic microbrain tumor identification method utilizing the VGG16 deep learning model. Mic...
The disease amyloid plaques, neurofibrillary tangles, synaptic dysfunction, and neuronal death gradually accumulate throughout Alzheimer's disease (AD), resulting in cognitive decline and functional disability. The challenges of dataset quality, inte...
Lymph-node status is important in decision-making during early gastric cancer (EGC) treatment. Currently, endoscopic submucosal dissection is the mainstream treatment for EGC. However, it is challenging for even experienced endoscopists to accurately...
Colon cancer poses a significant threat to human life with a high global mortality rate. Early and accurate detection is crucial for improving treatment quality and the survival rate. This paper presents a comprehensive approach to enhance colon canc...
Lung cancer is the most common causes of death among all cancer-related diseases. A lung scan examination of the patient is the primary diagnostic technique. This scan analysis pertains to an MRI, CT, or X-ray. The automated classification of lung ca...
One of the most popular fruits worldwide is the banana. Accurate identification and categorization of banana diseases is essential for maintaining global fruits security and stakeholder profitability. Four different types of banana leaves exist Healt...
Ultrasound images are susceptible to various forms of quality degradation that negatively impact diagnosis. Common degradations include speckle noise, Gaussian noise, salt and pepper noise, and blurring. This research proposes an accurate ultrasound ...
Ultrafast fluorescent confocal microscopy is a hypothetical approach for breast cancer detection because of its potential to achieve instantaneous, high-resolution images of cellular-level tissue features. Traditional approaches such as mammography a...
Breast cancer is a major health threat, with early detection crucial for improving cure and survival rates. Current systems rely on imaging technology, but digital pathology and computerized analysis can enhance accuracy, reduce false predictions, an...