LSTM-NAS-Net: Enhanced Brain Tumor Segmentation in MRI and CT Images using LSTM-Autoencoder-based Neural Architecture Search.

Brain Tumour (BT) a mass or a lump or a growth which occurs due to abnormal cell division or unusual growth of cells in the brain tissue. Initially, the two major types of BT are Primary BT and Secondary BT, the tumour that originate from brain is known as Primary BT and it may be cancerous or non-cancerous. The tumour the initiates from other part of the body and spreads to the brain is stated as secondary BT. Diagnosing BT generally involves a multiple investigation method, such as MRI, CT, PET, SPECT as well as the neurological examinations and blood investigations, whereas some of the patients may need biopsies to evaluate the tumour size and stage. Here we use MRI and CT images for BT segmentation whereas these modalities play a major role in diagnosing, treating, planning and monitoring the BT patients. Moreover, the multimodal data can provide a quantitative information's about the tumour size, shape, volume and texture. While segmenting the BT the lack of segmentation methods and the interpretability of the segmented regions are limited. To overcome this, we propose a novel LSTM autoencoder bas NAS method which is used for the extracting the BT features and these features can be fused using Contextual Integration Module (CIM) and segmented using the Segmentation Guided Regulizer (SGR) which helps to overcome the stated issues. Finally, the performance metrices are calculated by comparing with the state-of -the -art methods and our method achieves a best segmenting metrices. [ABSTRACT FROM AUTHOR]