eng Oriental Scientific Publishing Company Biomedical and Pharmacology Journal 0974-6242 2021-03-30 14 1 283 293 10.13005/bpj/2124 36891 article Sarika A Panwar 1 Mousami V Munot 2 Suraj Gawande 3 Pallavi S Deshpande 4 Department of Electronics and Telecommunication Engineering, AISSMS-IOIT,Pune-411001 Department of Electronics and Telecommunication Engineering, PICT, Pune Senior Application Engineer, Design Tech Systems Pvt. Ltd, Pune Department of Electronics and Telecommunication Engineering, SAE, Kondhwa, Pune Introduction: The World Brain Tumor Day is seen on eighth June, in a year. Despite exhaustive research in the medical field, the prevalence of this deadly disease is increasing globally with over new 28,000 braintumor cases being reported annually, in India alone. Recent advancements in the field of machine learning facilitate minimally invasive, efficient and reliable procedures for the diagnosis of Brain tumor.  Objective: This research intends to design and devlop a reliable framework for accurate diagnosis of brain tumor mainly meningioma type, gliomatype  and pituitary cerebrum tumor  utilizing Magnetic Resonance Imaging (MRI), one of the most mainstream non-obtrusive procedure  Methods: In the proposed system, pre-trained AlexNet is used to classify meningioma, glioma and pituitary brain tumor. The concept of transfer learning is applied using AlexNet for extracting the features from brain MRI images.The AlexNet contains eight layers in which the first five are convolution layer and the remaining three are fully connected layers. The last layer is a softmax layer which gets the output from fully connected layers. The ReLU non-linearity is applied to the output of every convolution and fully connected layer. The idea of transfer learning is applied utilizing AlexNet for computing  thefeatures from  brain MRI pictures. The AlexNet contains eight layers in which the initial five are convolution layers and the staying three layers are fully connected layers. a softmax is the last layer , which is feeded by the fully connected layers. The ReLUactivation function  is applied to the output of convolution layer and fully connectedlayer Result: The proposed system framework recorded the best order precision of 100 % to classify the brain tumor when validated using a practical dataset. Conclusion: The proposed work presents accurate and automatic brain tumor classification using transfer learning. The features extracted using AlexNet has proven to be efficient in obtaining good discriminative power in diagnosis of brain tumor https://biomedpharmajournal.org/vol14no1/a-reliableand-an-efficient-approach-for-diagnosis-of-brain-tumor-using-transfer-learning/ Brain Tumor Diagnosis CNN Deep Network