Medical Image Segmentation Using Deep Learning Method

Image segmentation is a subset of digital image processing that has various uses in the areas of visualisation, augmented reality, machine vision, and many more. The segmentation of organs, diseases, or abnormalities in medical images has gotten challenging as the field of medical image analysis develops. The segmentation of medical images aids in regulating the amount of medications and radiation exposure, as well as preventing the progression of diseases like tumours. Due to the many artefacts inherent in the images, medical image segmentation is a tremendously difficult task. Deep learning models have recently been shown useful for a variety of picture segmentation tasks. Due to the successes and excellent performance of the deep learning algorithms, this noteworthy growth has occurred. To diagnose brain tumours, medical practitioners frequently employ multimodal brain scans, utilising knowledge from the axial, coronal, and sagittal viewpoints, as an illustration. Brain tumour identification and segmentation using MR imaging are challenging and crucial for the medical community. To enable doctors to select the most appropriate treatments and perhaps save lives, brain tumours should be located and detected as soon as feasible. Deep learning algorithms have caught the interest of researchers in medical imaging because of their ability to support precise diagnosis, prognosis, and medical treatment technologies. Magnetic resonance (MR) images of 2D brain tumours are segmented in this study using deep neural networks (DNN) and data augmentation techniques. In recent times, deep learning has been revealed to be quite significant in the field of computer vision. By using it to diagnose diseases, less human judgement is required. Excellent accuracy is needed, especially for diagnosing brain tumours, when even the tiniest errors in judgement could have disastrous consequences. As a result, it is challenging to segment brain tumours from a medical standpoint. These days, there are numerous tumour segmentation methods accessible, but none of them are highly precise. The segmentation of brain tumours using deep learning is demonstrated in this article.