A major research subject in recent times is Alzheimer’s disease (AD) due to the growth and considerable societal impacts on health. So, the detection of AD is essential for medication care. Early detection of Alzheimer’s disease (AD) is critical for effective treatment and monitoring the time period between normal aging’s unavoidable cognitive loss & dementia’s more catastrophic degradation is common practice. The deep learning method for early diagnosis and automated categorization of Alzheimer’s disease (AD) has suddenly gained a lot of attention since rapid advancement in the field of GANs approaches has now been used in the clinical research sector. Many recent studies using brain MRI images and convolutional neural networks (CNN) to identify Alzheimer’s disease have yielded promising results. Instead of adequately engaging with the lack of Real data, many research papers have focused on prediction. The main purpose of this paper is to do this by generating synthetic MRI images using a series of DCGANs. This paper demonstrates the effectiveness of this concept by cascading DCGANs that imitate different stages of Alzheimer’s disease & utilizing SRGANs to enhance the resolution of MRI scans. The purpose of this research is to come forward and tell if an individual might just get Alzheimer’s disease. CNN, DCGANs, and SRGANs are used in this paper to present a Deep Learning-based approach that improves classification and prediction accuracy to 99.7% and also handles the lack of data and the resolution of data.
Classification is the grouping of information or objects in predefined labeled categories based on similarities. Exponential growth rates of scientific document collection leads to unmanageable manual classification. Feature extraction is the central prerequisite of automatic document classification. TF-IDF (term frequency-inverse document frequency) is commonly used to express the text feature weight. This paper proposes a new feature weighting method by modifying TF-IDF formula.
Machine Learning (ML) techniques play an important role in the medical field. Early diagnosis is required to improve the treatment of carcinoma. During this analysis Breast Cancer Coimbra dataset (BCCD) with ten predictors are analyzed to classify carcinoma. In this paper method for feature selection and Machine learning algorithms are applied to the dataset from the UCI repository. WEKA (“Waikato Environment for Knowledge Analysis”) tool is used for machine learning techniques. In this paper Principal Component Analysis (PCA) is used for feature extraction. Different Machine Learning classification algorithms are applied through WEKA such as Glmnet, Gbm, ada Boosting, Adabag Boosting, C50, Cforest, DcSVM, fnn, Ksvm, Node Harvest compares the accuracy and also compare values such as Kappa statistic, Mean Absolute Error (MAE), Root Mean Square Error (RMSE). Here the 10-fold cross validation method is used for training, testing and validation purposes.
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