FV18 Towards epileptogenesis staging with deep neural networks

Lu, Diyuan; Bauer, Sebastian; Neubert, Valentin; Costard, Lara S.; Rosenow, Felix; Triesch, Jochen

doi:10.1016/j.clinph.2019.12.108

Cited by 1 publication

(2 citation statements)

References 26 publications

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“…Input for this layer is received from output of pooling or subsampling, the attened output is fed as input which is given in (5), unrolling of all three-dimensional value output given by pooling is converted to vector is attening.…”

Section: Fully Connected Layermentioning

confidence: 99%

“…An intra-cranial electroencephalography is used to identify an issue which is occurred due to EPG by time frame. In [5] Machine Learning (ML) scheme is incorporated with electroencephalography to get an accurate development of EPG and supports for classi cation to impending behaviour. The general schematic of immune cell computation is described in the below Fig.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning neural network (DLNN)-based classification and optimization algorithm for organ inflammation disease diagnosis

et al. 2023

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Diagnostic image volume and complexity in healthcare system increases in rapid pace where available human pro ciency may not su cient for interpreting this much capacity of image data. Machine learning approaches exposed excessive potential to knob huge amount of two-dimensional annotated images of common illnesses from large databases. Deep learning imitates human for extracting knowledge from dataset and favourable to data scientists for accumulating, analysing, interpreting and predictive modelling. In this paper organ in ammation disease is addressed with Deep Learning Neural Network (DLNN) based classi cation scheme is incorporated to diagnose or prognoses the patient from severity, based on their historical database. In pandemic environment collecting histopathology tissue score is time consuming process due to a smaller number of physician availability, by implementing proposed DLNN algorithm suits for collecting organ in ammation score and categorizing its brutality by classi cation of pancreatitis, duodenum and appendix. In order to achieve accuracy and sensitivity of various stages soreness DLNN based algorithm is developed and it supports by classifying the datasets.

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Section: Fully Connected Layermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%