Improved Multiclass Brain Tumor Detection via Customized Pretrained EfficientNetB7 Model

A brain tumor considered the deadliest disease in the world. Patients with misdiagnoses and insufficient treatment have a lower chance of surviving for life. However, for diagnosing the disturbance in the brain, magnetic resonance images play a vital role, but due to the large number of images produced by MRI, it is time-consuming and difficult to diagnose a patient at an earlier stage with high accuracy. Brain tumor detection is challenging due to variations in its size, location, and similarity in health tissues. Further, early detection of brain tumors plays a vital role in enabling a wider range of treatment options, improvements in survival rate, and patients quality of life with reduced healthcare costs. That’s why there is a need for an automatic, accurate system that can detect brain tumor at an earlier stage with an accurate result. So, in this research article, pretrained modals such as AlexNet, VGG16, VGG19, ResNet50, InceptionV3, DenseNet121, and all variants of the EfficientNet model were chosen because of their exceptional performance in tasks like feature extraction and image classification with the capability of detecting anomalies in images. The experiment was done on a publicly available multiclass dataset for brain tumor. Further, before passing the dataset to the model, preprocessing was applied to the dataset in terms of resizing the images to the same size; further images were cropped due to the extra boundaries around the images; noise was removed from the images using the FastNIMeans Denoising colored filter; and a data augmentation technique was applied to reduce the overfitting problem and ensure accurate fast training. On different pretrained deep learning models, the EfficientNetB7 model produced better results in terms of validation accuracy. Further pretrained EfficientNetB7 model were customized by adding a few layers and fine-tuning parameters. Our customized pretrained EfficientNetB7 (CPEB7) model was evaluated in terms of accuracy, loss, precision, sensitivity, specificity, recall, f1-score, and MIOU (mean intersection over union) and the model achieved accuracy of 98.57%, 98.97%, 98.97%, and 99.38% for no, meningioma, pituitary, and glioma tumor classes, respectively. Further, our proposed model achieved an overall accuracy of 98.97% with a 1.02% miss classification rate and MIOU of 95.73%. Further, the proposed model was also evaluated on k-fold cross-validation and achieved 99.097% accuracy on fold-5. This proposed system shows superiority over already existing methods in terms of performance evaluation parameters.