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Brain tumor segmentation is a challenging problem in medical image analysis.The endpoint is to generate the salient masks that accurately identify braintumor regions in an fMRI screening. In this paper, we propose a novel attentiongate (AG model) for brain tumor segmentation that utilizes both the edgedetecting unit and the attention gated network to highlight and segment thesalient regions from fMRI images. This feature enables us to eliminate thenecessity of having to explicitly point towards the damaged area(externaltissue localization) and classify(classification) as per classical computervision techniques. AGs can easily be integrated within the deep convolutionalneural networks(CNNs). Minimal computional overhead is required while the AGsincrease the sensitivity scores significantly. We show that the edge detectoralong with an attention gated mechanism provide a sufficient enough method forbrain segmentation reaching an IOU of 0.78

AGD-Autoencoder: Attention Gated Deep Convolutional Autoencoder for Brain Tumor Segmentation