Coupled convolution layer for convolutional neural network

Uchida, Kazutaka; Tanaka, Masayuki; Okutomi, Masatoshi

doi:10.1016/j.neunet.2018.05.002

Cited by 44 publications

(9 citation statements)

References 19 publications

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“…Colors are shown as a color intensity, such as red, green, blue, yellow and black. The convolutional layer is flattened, and its function is to transform a multidimensional vector into a one-dimensional vector [ 38 ].…”

Section: Materials and Methodologymentioning

confidence: 99%

Analyzing Malaria Disease Using Effective Deep Learning Approach

2020

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Medical tools used to bolster decision-making by medical specialists who offer malaria treatment include image processing equipment and a computer-aided diagnostic system. Malaria images can be employed to identify and detect malaria using these methods, in order to monitor the symptoms of malaria patients, although there may be atypical cases that need more time for an assessment. This research used 7000 images of Xception, Inception-V3, ResNet-50, NasNetMobile, VGG-16 and AlexNet models for verification and analysis. These are prevalent models that classify the image precision and use a rotational method to improve the performance of validation and the training dataset with convolutional neural network models. Xception, using the state of the art activation function (Mish) and optimizer (Nadam), improved the effectiveness, as found by the outcomes of the convolutional neural model evaluation of these models for classifying the malaria disease from thin blood smear images. In terms of the performance, recall, accuracy, precision, and F1 measure, a combined score of 99.28% was achieved. Consequently, 10% of all non-dataset training and testing images were evaluated utilizing this pattern. Notable aspects for the improvement of a computer-aided diagnostic to produce an optimum malaria detection approach have been found, supported by a 98.86% accuracy level.

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Section: Materials and Methodologymentioning

confidence: 99%

Analyzing Malaria Disease Using Effective Deep Learning Approach

2020

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“…The CNN method was identified as the best classification method, corroborating the superior performance of CNNs in similar machine learning tasks. 35 The CNN is a kind of advanced feedforward neural network with convolution calculation and depth structure, which is one of the representative algorithms of deep learning. 17,18 The CNN has the ability to represent learning and can provide a shift-invariant classification according to its hierarchical structure.…”

Section: Discussionmentioning

confidence: 99%

Prediction of the taxonomical classification of theRanunculaceaefamily using a machine learning method

et al. 2022

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“…This CNN process significantly resembles the analysis of a visual scene in the biological neural circuits, also demonstrated in Figure 4. [119][120][121][122] Likewise, in the speech recognition, a voice can be decomposed into a series of basic signal structures, and the final identified voice feature is based on the convolution operations on sub-features. Roughly speaking, the CNNs with more than three convolution layers can be termed as deep neural networks (DNNs), which have been pioneered by Hinton et al in 2006.…”

Section: Algorithms Of Artificial Neural Circuitsmentioning

confidence: 99%

Bio‐Inspired 3D Artificial Neuromorphic Circuits

Liu

Wang

et al. 2022

Adv Funct Materials

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Neuromorphic circuits emulating the bio‐brain functionality via artificial devices have achieved a substantial scientific leap in the past decade. However, even with the advent of highly advanced bio‐inspired algorithms, the artificial intelligence based on current neuromorphic circuits is lagging behind significantly when compared with naturally evolved biological neural circuits. This massive and intriguing discrepancy is partly due to the incomprehensive understanding of bio‐brain operating mechanism, which relies heavily on the extremely complexed entangled 3D hierarchical neural networks. Configuring 3D neuromorphic hardware with combined computing and memory functionalities, coupled with compatible progress of software algorithms, can be an inevitable route to surmount the limitation encountered by current 2D artificial circuits. Herein, referring to the neuron configuration in 3D perspective together with detailed signal generation and propagation mechanism, the von Neumann configuration is compared with state‐of‐the‐art in‐memory computing architecture, and the development and perspectives of 3D in‐memory computing neuromorphic circuits are highlighted.

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Coupled convolution layer for convolutional neural network

Cited by 44 publications

References 19 publications

Analyzing Malaria Disease Using Effective Deep Learning Approach

Analyzing Malaria Disease Using Effective Deep Learning Approach

Prediction of the taxonomical classification of theRanunculaceaefamily using a machine learning method

Bio‐Inspired 3D Artificial Neuromorphic Circuits

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