  This paper presents a research study on the use of Convolutional Neural
Network (CNN), ResNet50, InceptionV3, EfficientNetB0 and NASNetMobile models to
efficiently detect brain tumors in order to reduce the time required for manual
review of the report and create an automated system for classifying brain
tumors. An automated pipeline is proposed, which encompasses five models: CNN,
ResNet50, InceptionV3, EfficientNetB0 and NASNetMobile. The performance of the
proposed architecture is evaluated on a balanced dataset and found to yield an
accuracy of 99.33% for fine-tuned InceptionV3 model. Furthermore, Explainable
AI approaches are incorporated to visualize the model's latent behavior in
order to understand its black box behavior. To further optimize the training
process, a cost-sensitive neural network approach has been proposed in order to
work with imbalanced datasets which has achieved almost 4% more accuracy than
the conventional models used in our experiments. The cost-sensitive InceptionV3
(CS-InceptionV3) and CNN (CS-CNN) show a promising accuracy of 92.31% and a
recall value of 1.00 respectively on an imbalanced dataset. The proposed models
have shown great potential in improving tumor detection accuracy and must be
further developed for application in practical solutions. We have provided the
datasets and made our implementations publicly available at -
https://github.com/shahariar-shibli/Explainable-Cost-Sensitive-Deep-Neural-Networks-for-Brain-Tumor-Detection-from-Brain-MRI-Images
