Prediction of Epilepsy Seizures by Machine Learning Methods

According to the Globe Health Organization (WHO), more than 50 million people throughout the world are living with a diagnosis of epilepsy, making it perhaps of the most widely recognized neurological issue. Epileptic seizures are a leading cause of hospitalization and mortality across the globe. Accurate and prompt diagnosis is more crucial than ever given the increase in epileptic seizures all through the globe and their effect on individuals' lives. Epilepsy, cancer, diabetes, heart disease, thyroid disease, and many more are only some of the diseases for which machine learning approaches are being applied in prediction and diagnosis. Epilepsy is one ailment that may be treated early on to save a person's life. The main objective of this research is to use feature label extraction to the dataset in order to obtain the best ML models for epileptic seizures. In order to predict epilepsy, we used the techniques of logistic regression, SVM, linear SVM, KNN, and RNN in this study. The models employed in this research are accurate to varying degrees and have attributes including precision, recall, f1-score, and support. This study demonstrates that the model is able to accurately predict the occurrence of epilepsy. Our discoveries demonstrate that involving Examination highlight extraction in the dataset, the Regional Neural Network (RNN) model with 99.9998 % Training data accuracy and 97.78% Test data accuracy and 100% prediction probability of epilepsy seizure produces the best results and also the feature characteristics of RNN is better as compared to other models used in current research work

MEDICAL IMAGE FUSION USING CURVELET TRANSFORM

ABSTRACT The paper analyses the characteristics of the Fast Discrete Curvelet Transform and put forward an image fusion algorithm based on Discrete Wavelet Transform and the Fast Discrete Curvelet Transform. The Curvelet Transform is a new approach in the image fusion techniques adding a new, lesser redundant, fast and simple way of dealing the images especially at the edges and curves and hence it is very suitable for the analysis of various natural images like Medical images using tomographic images like MRI and CT scan, seismic images, satellite pictures for the weather monitoring etc. The experimental results show that the method could extract useful information from the source images to fused images so that clear images are obtained. In choosing the low-frequency coefficients, the concept of local area variance was applied to the measuring criteria. In choosing the high frequency coefficients, the window property and local characteristics of pixels were analysed. Finally, the proposed algorithm was applied to experiments of multi-focus image fusion and complementary image fusion

Fingerprint Verification using Steerable Filters”,

ABSTRACT In this paper, fingerprint verification using steerable filters is presented. The existing fingerprint recognition systems are based on minutiae matching. The common fingerprint matching schemes are Correlation-based matching, Minutiae-based matching and Ridge feature -based matching. The minutiae-based matching systems are the most widely used and popular. The minutiae extraction undergoes very critical steps (like binerization, thinning) and which affects on the overall accuracy of the system. Poor ridge structure and the image processing articrafts may introduce spurious minutiae. A frequency selective as well as orientation selective transform like Gabor transform has been used for extracting the texture features. This paper describes a novel approach based on steerable wedge filter. The proposed method is capable of finding the texture features of fingerprint image irrespective to the image quality in terms of average gray level, clarity in the ridges and comparatively with fewer computations