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MR brain image categorization has been an active research domain from the last decade. Several techniques have been devised in the past for MR image categorization, starting from classical to the deep learning methods like convolutional neural networks (CNNs). Classical machine learning methods need handcrafted features to perform classification. The CNNs, on the other hand, perform classification by extracting image features directly from raw images via tuning the parameters of the convolutional and pooling layer. The features extracted by CNN strongly depend on the size of the training dataset. If the training dataset is small, CNN tends to overfit after several epochs. So, deep CNNs (DCNNs) with transfer learning have evolved. The prime objective of the present work is to explore the capability of different pre-trained DCNN models with transfer learning for pathological brain image classification. Various pre-trained DCNNs, namely Alexnet, Resnet50, GoogLeNet, VGG-16, Resnet101, VGG-19, Inceptionv3, and InceptionResNetV2, were used in the present study. The last few layers of these models were replaced to accommodate new image categories for our application. These models were extensively evaluated on data from Harvard, clinical, and benchmark Figshare repository. The dataset was then partitioned in the ratio 60:40 for training and testing. The validation on the test set reveals that the pre-trained Alexnet with transfer learning exhibited the best performance in less time compared to other proposed models. The proposed method is more generic as it does not need any handcrafted features and can achieve an accuracy value of 100%, 94%, and 95.92% for three datasets. Other performance measures used in the study include sensitivity, specificity, precision, false positive rate, error, F-score, Mathew correlation coefficient, and area under the curve. The results are compared with both the traditional machine learning methods and those using CNN. [ABSTRACT FROM AUTHOR]