Abstract:
MRI scans can be used to diagnose some types of brain diseases, which can lead to early
treatment of the diseases. This study employs deep learning approaches to develop robust
models for automated brain disease detection using a dataset sourced from Kaggle,
comprising 8,212 MRI images categorized into four classes: not a tumor, pituitary tumor,
meningioma, and glioma. Two novel deep architectures of CNN, named CNN01 and
CNN02, are designed and tested in the experiments, and compared with the classical
models, namely, Xception, DenseNet121, and ResNet50. Comparing the results of all the
tested models, CNN02 gets the highest score of 94.04%. Thus, CNN02 shows higher
effectiveness in extracting and categorizing features pertinent to brain MRI images. This
points to the fact that CNN designs should be customized to suit the medical imaging
diagnostic tasks in order to produce the best outcomes. Three models, namely, Xception
that utilizes depth-wise separable convolutions, DenseNet121 that utilizes densely
connected layers and ResNet50 that uses residual connections serve as the points of
reference. CNN01 and CNN02 have been designed with specific modifications in
architecture and such a network proves that there is need to modify the original network to
improve its performance. These findings have important implications for the health sector
and especially for facilitating the correct diagnosis of diseases so that appropriate treatment
can be provided on time. Possible future research directions include applying ensemble
learning methods that combine multiple classifiers, integrating complex data modalities,
and developing explanatory models that could improve physicians’ diagnostic accuracy
and awareness. In conclusion, this work demonstrates the possibilities of using deep
learning methods for efficient and accurate automated diagnose in the near future and in
the broader field of neuroimaging and medicine.
