Deep Learning-Based Brain Tumors Diagnosis

The diagnosis of brain tumor disease has always been a major medical challenge. Traditional brain tumor identification methods usually require manual image processing and have limited accuracy. Therefore, researchers have been looking for new methods to improve the accuracy and efficiency of brain tumor identification. Alfa Cytology offers deep learning-based brain tumor disease detection methods with high efficiency and accuracy, bringing new options to the field of brain science.

We provide deep learning-based methods for brain tumor disease detection and classification

Classification of brain tumors is a very crucial step after detection of the tumor to develop an effective treatment plan. We can detect and classify brain tumors by using deep learning techniques. We employ a multiscale deep convolutional neural network (multiscale DCNN) to process the input image using independent neural networks at different spatial scales. This allows us to accurately identify MRI scans containing three different types of tumors including gliomas, meningiomas, and pituitary tumors without the need to pre-process the images.

Analysis process

A dataset containing human brain MRI images which contains both tumor and non-tumor MRI images of the brain is used in this process. After pre-processing the data, we apply various image processing techniques such as filtering, blurring, cropping, etc. to divide the dataset into training and test sets. They are subjected to data enhancement by multiple stochastic transformations. The pre-trained dataset is fed into a CNN model which then detects the presence of tumor. If the tumor is present, it can be further classified into three categories.

Technical advantages

Brain tumors are a serious disease and early detection and accurate diagnosis are crucial for patient treatment and recovery. Alfa Cytology utilizes a deep learning approach to achieve efficient and accurate brain tumor recognition through the use of multiscale DCNN. Not only does it improve the accuracy of recognition compared to traditional methods, but it also drastically increases processing speed, providing you with faster and more accurate classification results. Please feel free to contact us to learn more about this more promising tool for brain tumor diagnosis.