Abstract
•Paper presents deep learning based medical image segmentation and classification methodology.•It provides an improved version of based method i.e. UNET.•Multi-disease detection task is resolved with comparable performance.•The proposed work is a step towards general AI.
Medical imaging and deep learning methods have significantly improved the early detection of brain diseases like tumors and Ischemic stroke with higher accuracy. Machine learning methods especially neural-network based algorithms have shown huge success in medical image analysis for variety of tasks including the detection, segmentation and classification of brain tumors and Ischemic stroke. Usually, these models address one problem at a time which is considered as Artificial Weak Intelligence (AWI). There is the need to develop methods that can push the research towards strong or Artificial General AI (AGI) where a single model can solve multiple tasks.
In this work, we propose convolutional neural network based integrated model to detect and classify two brain diseases simultaneously i.e., tumors and Ischemic stroke. For this, a new dataset is created by merging two open-source datasets: BRATS 2015 and ISLES 2015. The designed network is an enhancement of encoder-decoder architecture based UNET where feature maps from one encoder block are fused with output of following encoder block to keep low-level fine-grained information intact and distinguish the overlapping features during encoding process in addition to UNET skip connections. The dataset is partitioned into training and validation sets on the ratio of 80:20 respectively with proportionate image inclusion in each training batch to address class imbalance issue. Average accuracy obtained through proposed model is 99.56%, 99.99% specificity, 99.59% precision and F1- score 99.57%. Obtained performance scores shows the usability of proposed feature fusion mechanism for multi-disease detection.