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Intracranial haemorrhage is a bleeding that occurs in brain which needs immediate medical attention and intensive medical care. The objective of this work is early detection of intracranial haemorrhage through automated model using DenseNets. DenseNets are used for processing MRI images and for detection of intracranial haemorrhage and its different variants. MRI scanned images samples are collected from a nearby neurology super speciality hospital. Segmentation of images is done through DenseNets which are also called deep connected convolution networks. Based on the image segments, the variant of intracranial haemorrhage is predicted. DenseNets layers are very narrow and as they add small set of feature maps and performs better when compared to the detection of the intracranial haemorrhage using convolution neural network (Juan et al in Proceedings of 4th congress on robotics and neuro science (2019), [1]). The accuracy of the proposed method is 91% achieved through the gradient from loss function which has access to each and every layer.