This study presents a convolutional neural network (CNN)-based approach for the multi-class classification of brain tumors using magnetic resonance imaging (MRI) scans. We utilize a publicly available dataset containing MRI images categorized into four classes: glioma, meningioma, pituitary tumor, and no tumor. Our primary objective is to build a light weight deep learning model that can automatically classify brain tumor types with high accuracy. To achieve this goal, we incorporate image preprocessing steps, including normalization, data augmentation, and a cropping technique designed to reduce background noise and emphasize relevant regions. The CNN architecture is optimized through hyperparameter tuning using Keras Tuner, enabling systematic exploration of network parameters. To ensure reliable evaluation, we apply 5-fold cross-validation, where each hyperparameter configuration is evaluated across multiple data splits to mitigate overfitting. Experimental results demonstrate that the proposed model achieves a classification accuracy of 98.78%, indicating its potential as a diagnostic aid in clinical settings. The proposed method offers a low-complexity yet effective solution for assisting in early brain tumor diagnosis.

Hyperparameters are configurations that determine the structure of machine learning models and control their learning processes. They shouldn't be confused with the model's parameters (such as the bias) whose optimal values are determined during training. Hyperparameters are adjustable configurations that are manually set and tuned to optimize the model performance. They are top-level parameters whose values contribute to determining the weights of the model parameters. The two main types of hyperparameters are the model hyperparameters (such as the number and units of layers) which determine the structure of the model and the algorithm hyperparameters (such as the optimization algorithm and learning rate), which influences and controls the learning process.

Let's get right into coding! We will import all necessary libraries, internal code, and specify callbacks, metrics, inputs, and loss function which will be passed to our custom HyperModel. Hypermodel is a class that allow us to use hp arguments to define hyperparameters. After initializing hypermodel, reassign directory name get_project_name()(where hpo trials will be saved) if already exists since if you run with already existing directory name keras tuner does not run properly. Another way to avoid this is to add overwrite True parameter when instantiating kt.RandomSearch.

Building machine learning models is an iterative process that involves optimizing the model’s performance and compute resources. The settings that you adjust during each iteration are called…

Hyperparameters are those tunable parameters which can directly affect how well a model trains and are set before the learning process begins. Examples of hyperparameters are — For your ML model, in…

In computer vision, we often build Convolution neural networks for different problems dealing with images like image classification, object detection, etc. In image classification tasks a CNN network is built using a combination of different convolution layers, pooling layers, dropouts, and at last fully connected layers. But while building this type of networks we define different sizes of kernels to extract feature maps and different neurons for different layers. We do not have a fixed rule of defining the number of layers, neurons, and kernel size. Keras Tuner is a library that resolves this problem and gives us the optimal parameters to attain high accuracy.

Posted by Tom O'Malley The success of a machine learning project is often crucially dependent on the choice of good hyperparameters. As machine learning continues to mature as a field, relying on trial and error to find good values for these parameters (also known as "grad student descent") simply doesn't scale. In fact, many of today's state-of-the-art results, such as EfficientNet, were discove…

You may hesitate to refer to this implementation's code as terribly complex, but when you compare it to the following projects I hope you change your mind. To see more details about the above code, the Keras Tuner process more generally, and what more you can do with the project, see its website. Next up is AutoKeras, which I will refer to as an "off the shelf" solution, one which is prepackaged and more or less ready to go, using a more restrictive code template.