Artificial Intelligence has changed our outlook towards the whole world, and it is regularly used to better understand all the data and information that surround us in our everyday lives. One such application of Artificial Intelligence in a real-world scenario is the extraction of data from various images and interpreting them in different ways. This includes applications like object detection, image segmentation, image restoration, etc. While every technique has its own area of application, image segmentation has a variety of applications extending from the complex medical field to regular pattern identification. The aim of this paper is to research about several FCNN-based semantic segmentation techniques to develop a deep learning model that is able to segment tumours in brain MRI images to a high degree of precision and accuracy. The aim is to try several different architectures and experiments with several loss functions to improve the accuracy of our model and obtain the best model for our classification including newer loss functions like dice loss function, hierarchical dice loss function cross entropy, etc.

人工智能改变了我们对整个世界的看法，它经常被用来更好地理解我们日常生活中周围的所有数据和信息。人工智能在现实场景中的应用之一是从各种图像中提取数据并以不同的方式解释它们。这包括对象检测、图像分割、图像恢复等应用。虽然每种技术都有其自己的应用领域，但图像分割具有从复杂的医学领域到常规模式识别的各种应用。本文的目的是研究几种基于 FCNN 的语义分割技术，以开发一种深度学习模型，能够高精度和准确地分割脑部 MRI 图像中的肿瘤。目的是尝试几种不同的架构并使用多种损失函数进行实验，以提高模型的准确性并获得分类的最佳模型，包括较新的损失函数，如骰子损失函数、分层骰子损失函数交叉熵等。