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- W4320080900 abstract "Neurological disorders represent anomalies relevant to the human nervous system. They also contain biochemical, anatomical and electrical modifications in the central nervous system, the spinal cord and the brain. These disorders provoke different symptoms. Early diagnosis of such changes is necessary for treatment, to limit disease progression. An accurate and CAD system is introduced in this paper to classify brain magnetic resonance images, which overcomes crucial problems in pattern classification, such as extracting certain features in the training phase. Our contribution is to merge a Second-Generation Wavelet Transform Network (SGWTN) and deep learning architectures, hence suggesting novel supervised feature extraction approaches for pattern classification. Our novel architecture allows the classification of all classes of our dataset by the reconstruction of a deep stacked SGWTN-Autoencoder (SGWTN-AE). Combining Curvelet Pooling (CP) with the Adam gradient calculation method can improve their accuracy. We implement CP with Adam in this work using the Haar curvelet (CurvPool-AH) and the Shannon Curvelet (CurvPool-AS). This Network is obtained after a series of SGWTN-AEs followed by a Softmax classifier at the last layer. We find that CurvPool performs fairly well on all datasets. However, overfitting is an issue with all the approaches considered. CP has the potential to outperform current approaches, particularly when merged with an adaptive gradient and curvelets chosen specifically for the data. Our architecture is tested with various image datasets. These latter are as follows: DS-66, DS-90, DS-160, and DS-255. Based on 5×5 cross-validation, our suggested approach can outperform the state-of-the-art methods in terms of classification accuracy." @default.
- W4320080900 created "2023-02-12" @default.
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- W4320080900 date "2023-05-01" @default.
- W4320080900 modified "2023-09-27" @default.
- W4320080900 title "Deep second generation wavelet autoencoders based on curvelet pooling for brain pathology classification" @default.
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- W4320080900 doi "https://doi.org/10.1016/j.bspc.2023.104675" @default.
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