Aditya Raj Sahoo

Bio: Aditya Raj Sahoo is an academic researcher. The author has contributed to research in topics: Computer science. The author has an hindex of 1, co-authored 2 publications receiving 1 citations.

Image Enhancement using ESRGAN for CNN based X-Ray Classification

Abstract: There is tremendous amount of computational power in artificial intelligence models like computing variety of complex mathematical calculations and recognizing objects. In the past six to seven years, the amount of computing power used by record-breaking AI models doubled frequently in the time span of months. An interesting way in which these models learn and progress is through deep learning. Deep learning is an intelligent machine’s way in which machines learn without being supervised by us and grants them the power to recognize speech, translate, and even make or take data-driven decisions. Machines consider this as a studying method, inspired by the architecture of the human brain and how we learn. An important deep learning method where we train the machines on information that is unlabeled is called unsupervised learning. A strong part of neural networks that are utilized for unsupervised learning is Generative Adversarial Networks. When it comes to applications on images quality improvement, Super Resolution GAN (SRGAN) have a key role to play in it. It was proposed by researchers at Twitter. The motive of this GAN is to super resolute the fine textures of the image by upscaling it. In order to enhance the images further, ESRGAN is used. As the name suggests, ESRGAN is an implementation of SRGAN and uses some added components of SRGAN.

Brain tumor based mri image enhancement using entropy and clahe based intuitionistic fuzzy method with deep learning

Abstract: The inner area of the human brain is where abnormal brain cells gather when they become a mass. These are known as brain tumors, and based on the location and size of the tumor, they can produce a wide range of symptoms. Accurate segmentation and classification of brain tumors are critical for effective diagnosis and treatment planning. In this paper, we present a novel approach for the segmentation and classification of brain tumors using Entropy and CLAHE Based Intuitionistic Fuzzy Method with Deep Learning. Entropy and CLAHE (Contrast Limited Adaptive Histogram Equalization) based Intuitionistic Fuzzy Method with Deep Learning is a technique that combines several image processing and machine learning algorithms to enhance the quality of images. By applying entropy-based techniques to an image, we can identify and highlight the most significant features or patterns in the image. Our study provides a thorough evaluation of the proposed technique and its performance compared to other methods, showing its effectiveness and potential for use in real-world applications. Our method separates the tumor regions from the healthy tissue and provides accurate results in comparison with traditional methods. The results of this study demonstrate the potential of this approach to improve the diagnosis and treatment of brain tumors and provide a foundation for future research in this field. The proposed technique holds significant promise for improving the prognosis and quality of life for patients with brain tumors.

Brain tumor based mri image enhancement using entropy and clahe based intuitionistic fuzzy method with deep learning

Abstract: The inner area of the human brain is where abnormal brain cells gather when they become a mass. These are known as brain tumors, and based on the location and size of the tumor, they can produce a wide range of symptoms. Accurate segmentation and classification of brain tumors are critical for effective diagnosis and treatment planning. In this paper, we present a novel approach for the segmentation and classification of brain tumors using Entropy and CLAHE Based Intuitionistic Fuzzy Method with Deep Learning. Entropy and CLAHE (Contrast Limited Adaptive Histogram Equalization) based Intuitionistic Fuzzy Method with Deep Learning is a technique that combines several image processing and machine learning algorithms to enhance the quality of images. By applying entropy-based techniques to an image, we can identify and highlight the most significant features or patterns in the image. Our study provides a thorough evaluation of the proposed technique and its performance compared to other methods, showing its effectiveness and potential for use in real-world applications. Our method separates the tumor regions from the healthy tissue and provides accurate results in comparison with traditional methods. The results of this study demonstrate the potential of this approach to improve the diagnosis and treatment of brain tumors and provide a foundation for future research in this field. The proposed technique holds significant promise for improving the prognosis and quality of life for patients with brain tumors.