Brain tumor multi classification and segmentation in MRO images using deep learning

This study proposes a deep learning model for the classification and segmentation of brain tumors from magnetic resonance imaging (MRI) scans. The classification model is based on the EfficientNetB1 architecture and is trained to classify images into four classes: meningioma, glioma, pituitary adenoma, and no tumor. The segmentation model is based on the U-Net architecture and is trained to accurately segment the tumor from the MRI images. The models are evaluated on a publicly available dataset and achieve high accuracy and segmentation metrics, indicating their potential for clinical use in the diagnosis and treatment of brain tumors.

EfficientNet Algorithm for Classification of Different Types of Cancer

Accurate and efficient classification of different types of cancer is critical for early detection and effective treatment. In this paper, we present the results of our experiments using the EfficientNet algorithm for classification of brain tumor, breast cancer mammography, chest cancer, and skin cancer. We used publicly available datasets and preprocessed the images to ensure consistency and comparability. Our experiments show that the EfficientNet algorithm achieved high accuracy, precision, recall, and F1 scores on each of the cancer datasets, outperforming other state-of-the-art algorithms in the literature. We also discuss the strengths and weaknesses of the EfficientNet algorithm and its potential applications in clinical practice. Our results suggest that the EfficientNet algorithm is well-suited for classification of different types of cancer and can be used to improve the accuracy and efficiency of cancer diagnosis.

Brain Tumor classification and Segmentation using Deep Learning

Brain tumors are a complex and potentially life-threatening medical condition that requires accurate diagnosis and timely treatment. In this paper, we present a machine learning-based system designed to assist healthcare professionals in the classification and diagnosis of brain tumors using MRI images. Our system provides a secure login, where doctors can upload or take a photo of MRI and our app can classify the model and segment the tumor, providing the doctor with a folder of each patient's history, name, and results. Our system can also add results or MRI to this folder, draw on the MRI to send it to another doctor, and save important results in a saved page in the app. Furthermore, our system can classify in less than 1 second and allow doctors to chat with a community of brain tumor doctors. To achieve these objectives, our system uses a state-of-the-art machine learning algorithm that has been trained on a large dataset of MRI images. The algorithm can accurately classify different types of brain tumors and provide doctors with detailed information on the size, location, and severity of the tumor. Additionally, our system has several features to ensure its security and privacy, including secure login and data encryption. We evaluated our system using a dataset of real-world MRI images and compared its performance to other existing systems. Our results demonstrate that our system is highly accurate, efficient, and easy to use. We believe that our system has the potential to revolutionize the field of brain tumor diagnosis and treatment and provide healthcare professionals with a powerful tool for improving patient outcomes.