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Papers by vinayagamoorthy .R

European Chemical Bulletin, 2023

Brains are enormous and complex organs that control our nervous systems and contain about 100 bil... more Brains are enormous and complex organs that control our nervous systems and contain about 100 billion nerve cells. The brain is an essential organ. A brain abnormality could put human health at risk. Tumors in the brain are among the most serious of these abnormalities. An uncontrollable growth of brain cells inside the skull causes this serious form of cancer. Generally, tumor cells exhibit heterogeneity, making them difficult to classify. In order to decide on the correct medication, it is essential that tumors are detected early, and their location, size, and types must also be assessed. Developing systems that incorporate human expertise is becoming increasingly popular using Soft Computing. Image processing and cytology are used more often to diagnose disease. Correct diagnosis is essential in treating and curing diseases. This paper proposes a fuzzy logic based brain tumor classification method that can be used for proper treatment planning. This paper provides detailed analysis of the advantages of the hybrid method, demonstrating the fact that when Neuro-Fuzzy Neural is paired with MResNet (NFMResnet), there is a significant increase in classification accuracy. The NFMResnet contains convolutional layers, pooling layers, and fully-connected layers, as well as a Fuzzy Self-Organization Layer. Using MResNet and fuzzy logic, the model handles uncertain and imprecise input patterns. Three independent steps are involved in training the NFMResnet. Three independent steps are involved in training the NFMResnet.

International Journal of Scientific Research and Review, 2019

Image segmentation has created important advances in recent years. Recent work construct to a gre... more Image segmentation has created important advances in recent years. Recent work construct to a great extent with respect to Deep Learning techniques that has brought about groundbreaking enhancement within the accuracy of segmentation. As a result of image segmentations are a midlevel illustration, they need a potential to create major contribution over the wide field of visual understanding from image classification and interactive pursuit. Medical image segmentation is a sub area of image segmentation that has many essential applications inside the prospect of medical image evaluation and diagnostic. In this paper, distinct strategies of medical image segmentation could be classified forthwith their sub techniques and sub fields. This paper presents useful approaches into the field of medical image segmentation using Deep Learning and attempt to summarize the long term scale of work.

The International journal of analytical and experimental modal analysis, 2020

In the recent years, brain tumor detection and segmentation by the MRI Images in the medical fiel... more In the recent years, brain tumor detection and segmentation by the MRI Images in the medical field has been very useful in recent years. Due to accuracy of the MRI Images the investigation can detect brain tumor in an exact manner. The MRI images help in the detection of unusual growth of tissues and blocks of blood in nervous system. The initial step in the detection of brain tumor comprises of the checking of symmetric and asymmetric shape of brain to define the abnormal development in the brain. After these steps, the next step is to segment the images. It is based on two technical processes. One is the F-Transform (Fuzzy Transform) technique and the other is Morphological operation technique. Basically, these two techniques are used to design the image in Magnetic Resonance Imaging. The design of the image can help to detect the precincts of brain, brain tumor and calculate the concrete area of the tumor in the brain. The f-transform method is used to give certain information like the rebuilt of missing edges and extracting the silent edges in the images. The accuracy and clarity of the MRI Images depends on each other. Hence, the study pursues a comparative study of the development of Brain Tumor segmentation and detection in MRI.

NeuroQuantology, 2022

The brain is considered one of the most important organs in human. A tumor in the brain can lead ... more The brain is considered one of the most important organs in human. A tumor in the brain can lead to loss of functionality. Approximately 18,020 deaths are occurred in 2020 due to brain tumors. It is possible to minimize these cases if a brain tumor is detected earlier. Brain tumors are the most widely recognized and forceful infection, prompting a short future and much aggravation. Patient's survival rates can be improved by timely and accurate diagnosis. The principle technique for distinguishing brain tumor is to investigate an MRI picture that gives definite data about brain shape and abnormality identification in brain tumor tissue with the quick advancement of Deep Learning (DL), particularly the improvement of PC vision innovation, programmed brain tumor recognition is proposed by utilizing ResNet. As the RESNET network deepens, its ability to extract network characteristics improves through layer-to-layer feature fusion. ResNet has proven to operate better than other deep learning models, as the research team tested it in an acceptable timeframe, and compared several deep learning models, which resulted in a higher accuracy with ResNet. In this paper, we propose another model called Modified Residual Network (MResNet) to classify brain tumor image. The proposed model has been assessed utilizing activation layer on an openly accessible brain tumor image dataset having selected feature images.Taking a more optimistic view is also possible with regards to skipping training in some residual block layers.There is no general consensus on the optimal number of layers required for neural networks, since the number of layers required may depend on the complexity of the dataset. The network will not be trained on layers that are unimportant and do not contribute to overall accuracy by including skip connections instead of treating the number of layers as an important hyperparameter to tune. We can use skip connections to make our neural networks dynamic to tune the number of layers during training optimally. Our model requires extremely less computational power and has much better precision results when contrasted with other pre-trained models. The proposed techniques performed well when compared to existing techniques like LeNet, AlexNet, VGG Net 16, ResNet 18.

International journal of health sciences, 2022

Deep Learning is a subdivision of machine learning and Artificial intelligence (AI). Autonomous D... more Deep Learning is a subdivision of machine learning and Artificial intelligence (AI). Autonomous Deep learning enables human brain to think and learn computers. In recent days Deep learning is used in many domains, especially in medical field. It is used mainly in classification. The Convolutional Neural Network (CNN) is one among the best technique in DL. It is best suitable in image classifications. CNN is directed to process the data into multiple layers of arrays. It is used for computationally efficient. Brain Tumor is one of the dangerous diseases in India as well as the whole world. A brain tumor is an unwanted cell in the brain. Brain tumor symptoms are based on size, location and type .There are two types of brain tumor. Brain tumor tissue affects on the brain that is called primary tumor. Brain tumor tissue affects in outside the brain that is called as secondary tumor (metastatic).In this paper, we are analyzing various Deep Convolution Neural Network on brain tumor perspe...

International Journal of Mechanical Engineering, 2022

The World Health Organization revealed that the brain tumor is one of the most severe sicknesses ... more The World Health Organization revealed that the brain tumor is one of the most severe sicknesses since it affects most people, including kids, worldwide. Developing a system to identify brain tumors at a beginning stage would assist in saving the existence of many people. Much exploration has been made around here to
develop a system for distinguishing brain tumors; however, this system should be improved, its exactness upgraded. Consequently, feature selection methods are expected to improve the system. The main intention of the feature selection techniques in machine learning (ML) is to select a suitable set of features. Wrapper
methods are used to filter. These methods are classified into four categories: forward selection, backward elimination, exhaustive feature selection, and recursive feature elimination. In recent years, brain tumor disease affected more people. Brain tumor disease affects the brain, sometimes sprit into some other parts. Besides, there are 55 features concentrated on, like the image roughness, consistency or energy, and nearby
homogeneity removed to show the quality distinction between methods. The goal is to search for the possibility of features that structure a large problem with feature selection techniques, which is resolved using boruta and genetics. Boruta feature selection algorithm based on random forest. In this paper, we introduced a hybrid feature selection technique called GenBoruta. GenBoruta is a hybrid feature selection algorithm for finding all relevant variables. It iteratively eliminates the features which are demonstrated by a measurable test to be less significant than random probes. The proposed techniques performed well compared to existing techniques like forwarding Selection, Backward Elimination, Boruta, and Genetic.

European Chemical Bulletin, 2023

Brains are enormous and complex organs that control our nervous systems and contain about 100 bil... more Brains are enormous and complex organs that control our nervous systems and contain about 100 billion nerve cells. The brain is an essential organ. A brain abnormality could put human health at risk. Tumors in the brain are among the most serious of these abnormalities. An uncontrollable growth of brain cells inside the skull causes this serious form of cancer. Generally, tumor cells exhibit heterogeneity, making them difficult to classify. In order to decide on the correct medication, it is essential that tumors are detected early, and their location, size, and types must also be assessed. Developing systems that incorporate human expertise is becoming increasingly popular using Soft Computing. Image processing and cytology are used more often to diagnose disease. Correct diagnosis is essential in treating and curing diseases. This paper proposes a fuzzy logic based brain tumor classification method that can be used for proper treatment planning. This paper provides detailed analysis of the advantages of the hybrid method, demonstrating the fact that when Neuro-Fuzzy Neural is paired with MResNet (NFMResnet), there is a significant increase in classification accuracy. The NFMResnet contains convolutional layers, pooling layers, and fully-connected layers, as well as a Fuzzy Self-Organization Layer. Using MResNet and fuzzy logic, the model handles uncertain and imprecise input patterns. Three independent steps are involved in training the NFMResnet. Three independent steps are involved in training the NFMResnet.

International Journal of Scientific Research and Review, 2019

Image segmentation has created important advances in recent years. Recent work construct to a gre... more Image segmentation has created important advances in recent years. Recent work construct to a great extent with respect to Deep Learning techniques that has brought about groundbreaking enhancement within the accuracy of segmentation. As a result of image segmentations are a midlevel illustration, they need a potential to create major contribution over the wide field of visual understanding from image classification and interactive pursuit. Medical image segmentation is a sub area of image segmentation that has many essential applications inside the prospect of medical image evaluation and diagnostic. In this paper, distinct strategies of medical image segmentation could be classified forthwith their sub techniques and sub fields. This paper presents useful approaches into the field of medical image segmentation using Deep Learning and attempt to summarize the long term scale of work.

The International journal of analytical and experimental modal analysis, 2020

In the recent years, brain tumor detection and segmentation by the MRI Images in the medical fiel... more In the recent years, brain tumor detection and segmentation by the MRI Images in the medical field has been very useful in recent years. Due to accuracy of the MRI Images the investigation can detect brain tumor in an exact manner. The MRI images help in the detection of unusual growth of tissues and blocks of blood in nervous system. The initial step in the detection of brain tumor comprises of the checking of symmetric and asymmetric shape of brain to define the abnormal development in the brain. After these steps, the next step is to segment the images. It is based on two technical processes. One is the F-Transform (Fuzzy Transform) technique and the other is Morphological operation technique. Basically, these two techniques are used to design the image in Magnetic Resonance Imaging. The design of the image can help to detect the precincts of brain, brain tumor and calculate the concrete area of the tumor in the brain. The f-transform method is used to give certain information like the rebuilt of missing edges and extracting the silent edges in the images. The accuracy and clarity of the MRI Images depends on each other. Hence, the study pursues a comparative study of the development of Brain Tumor segmentation and detection in MRI.

NeuroQuantology, 2022

The brain is considered one of the most important organs in human. A tumor in the brain can lead ... more The brain is considered one of the most important organs in human. A tumor in the brain can lead to loss of functionality. Approximately 18,020 deaths are occurred in 2020 due to brain tumors. It is possible to minimize these cases if a brain tumor is detected earlier. Brain tumors are the most widely recognized and forceful infection, prompting a short future and much aggravation. Patient's survival rates can be improved by timely and accurate diagnosis. The principle technique for distinguishing brain tumor is to investigate an MRI picture that gives definite data about brain shape and abnormality identification in brain tumor tissue with the quick advancement of Deep Learning (DL), particularly the improvement of PC vision innovation, programmed brain tumor recognition is proposed by utilizing ResNet. As the RESNET network deepens, its ability to extract network characteristics improves through layer-to-layer feature fusion. ResNet has proven to operate better than other deep learning models, as the research team tested it in an acceptable timeframe, and compared several deep learning models, which resulted in a higher accuracy with ResNet. In this paper, we propose another model called Modified Residual Network (MResNet) to classify brain tumor image. The proposed model has been assessed utilizing activation layer on an openly accessible brain tumor image dataset having selected feature images.Taking a more optimistic view is also possible with regards to skipping training in some residual block layers.There is no general consensus on the optimal number of layers required for neural networks, since the number of layers required may depend on the complexity of the dataset. The network will not be trained on layers that are unimportant and do not contribute to overall accuracy by including skip connections instead of treating the number of layers as an important hyperparameter to tune. We can use skip connections to make our neural networks dynamic to tune the number of layers during training optimally. Our model requires extremely less computational power and has much better precision results when contrasted with other pre-trained models. The proposed techniques performed well when compared to existing techniques like LeNet, AlexNet, VGG Net 16, ResNet 18.

International journal of health sciences, 2022

Deep Learning is a subdivision of machine learning and Artificial intelligence (AI). Autonomous D... more Deep Learning is a subdivision of machine learning and Artificial intelligence (AI). Autonomous Deep learning enables human brain to think and learn computers. In recent days Deep learning is used in many domains, especially in medical field. It is used mainly in classification. The Convolutional Neural Network (CNN) is one among the best technique in DL. It is best suitable in image classifications. CNN is directed to process the data into multiple layers of arrays. It is used for computationally efficient. Brain Tumor is one of the dangerous diseases in India as well as the whole world. A brain tumor is an unwanted cell in the brain. Brain tumor symptoms are based on size, location and type .There are two types of brain tumor. Brain tumor tissue affects on the brain that is called primary tumor. Brain tumor tissue affects in outside the brain that is called as secondary tumor (metastatic).In this paper, we are analyzing various Deep Convolution Neural Network on brain tumor perspe...

International Journal of Mechanical Engineering, 2022

The World Health Organization revealed that the brain tumor is one of the most severe sicknesses ... more The World Health Organization revealed that the brain tumor is one of the most severe sicknesses since it affects most people, including kids, worldwide. Developing a system to identify brain tumors at a beginning stage would assist in saving the existence of many people. Much exploration has been made around here to
develop a system for distinguishing brain tumors; however, this system should be improved, its exactness upgraded. Consequently, feature selection methods are expected to improve the system. The main intention of the feature selection techniques in machine learning (ML) is to select a suitable set of features. Wrapper
methods are used to filter. These methods are classified into four categories: forward selection, backward elimination, exhaustive feature selection, and recursive feature elimination. In recent years, brain tumor disease affected more people. Brain tumor disease affects the brain, sometimes sprit into some other parts. Besides, there are 55 features concentrated on, like the image roughness, consistency or energy, and nearby
homogeneity removed to show the quality distinction between methods. The goal is to search for the possibility of features that structure a large problem with feature selection techniques, which is resolved using boruta and genetics. Boruta feature selection algorithm based on random forest. In this paper, we introduced a hybrid feature selection technique called GenBoruta. GenBoruta is a hybrid feature selection algorithm for finding all relevant variables. It iteratively eliminates the features which are demonstrated by a measurable test to be less significant than random probes. The proposed techniques performed well compared to existing techniques like forwarding Selection, Backward Elimination, Boruta, and Genetic.