Brain Tumor Detection and Classification Using Artificial Intelligence
Abstract : Health is very important for human life. In particular, the health of the brain, which is the executive of the vital resource, is very important. Diagnosis for human health is provided by magnetic resonance imaging (MRI) devices, which help health decision makers in critical organs such as brain health.
Images from these devices are a source of big data for artificial intelligence. This big data enables high performance in image processing classification problems, which is a subfield of artificial intelligence. In this study, we aim to classify brain tumors such as glioma, meningioma, and pituitary tumor from brain MR images.
Convolutional Neural Network (CNN) and CNN-based inception-V3, EfficientNetB4, VGG19, transfer learning methods were used for classification. of such diseases.
F-score, recall, imprinting and accuracy were used to evaluate these models. The best accuracy result was obtained with VGG16 with 98%, while the F-score value of the same transfer learning model was 97%, the Area Under the Curve (AUC) value was 99%, the recall value was 98%, and the precision value was 98%.
CNN architecture and CNN-based transfer learning models are very important for human health in early diagnosis and rapid treatment
a method that detects if there is a tumor or not and then classifies the tumor type. The proposed method was tested on 150 T1-weighted MRI brain imaging for identifying brain tumors. The supervised approach was utilized for the classification process, and the principal component analysis was employed for feature extraction.
They also assessed the tumor’s area and volume to determine the tumor’s levels. The findings of the experiments demonstrate that KSVM is 97 percent accurate in classifying brain tumors.
Acquiring large and diverse datasets of medical images with annotated tumors can be challenging. Limited data can lead to models that are not sufficiently generalized.
Class imbalance between tumor and non-tumor cases can affect model performance, requiring techniques like data augmentation or sampling strategies.
Deep learning models, especially complex architectures like CNNs, can be difficult to interpret. Understanding how and why a model makes a particular prediction (e.g., identifying tumor regions) is crucial for medical acceptance and trust.
Lack of interpretability can hinder clinical adoption as healthcare professionals may be reluctant to trust "black-box" models without understanding their decision-making process.
Deep learning is a subset of machine learning that focuses on training artificial neural networks to perform complex tasks by learning patterns and representations directly from data. Unlike traditional machine learning approaches that require manual feature engineering, deep learning algorithms autonomously extract hierarchical features from data, leading to the creation of powerful and highly accurate models.
Convolutional neural networks represent a major breakthrough in deep learning and computer vision. These architectures are specifically designed to extract meaningful features from complex visual data, such as images and video. The inherent structure of the CNN, consisting of convolutional layers, pooling layers, and fully connected layers, mimics the ability of the human visual system to recognize patterns and hierarchical features. Convolutional layers use convolutional operations to detect local features, which are then progressively abstracted by pooling layers that condense the information. The resulting hierarchical representations are then fed into fully connected layers for classification or regression tasks. CNN have redefined the landscape of image recognition, achieving remarkable success in diverse domains ranging from image classification and object detection to face recognition and medical image
EfficientNet is a family of scalable and efficient CNN models. The main goal of this series is to achieve better performance with fewer parameters. The term "EfficientNet" is a combination of the words "efficiency" and "network". The model series is mainly used in visual processing tasks such as image classification.
EfficientNet is a family of models that delivers competitive results in both performance and computational cost. It offers variations of different size and complexity at different scales. Higher numbered models are typically larger and more complex, but require more computing power.
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