Morphological Convolutional Neural Network Architecture for Digit Recognition

Mellouli, Dorra; Hamdani, Tarek M.; Medina, Javier; Ayed, Mounir Ben; Alimi, Adel M.

doi:10.1109/tnnls.2018.2890334

Cited by 69 publications

(36 citation statements)

References 14 publications

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“…In this context, the intrinsic linear combination operations within the CNN model can be replaced by nonlinear morphological operations to reduce the number of activation functions, while maintaining (or even increasing) the performance of the model. In addition, Mellouli et al have defined a soft version of dilation and erosion using Counter-Harmonic Mean (CHM), validating the method in digits recognition, where the proposed CHM-based layer achieved higher performance than conventional models [21]. Also, Nogueira et al [22] conducted an extensive study on the combination of deep models and multiple morphological operations such as opening, closing, top-hat operations, which have been combined with CNN to perform classification task on aerial images.…”

Section: A Related Workmentioning

confidence: 99%

Morphological Convolutional Neural Networks for Hyperspectral Image Classification

Roy¹,

Mondal

Paoletti

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Convolutional neural networks (CNNs) have become quite popular for solving many different tasks in remote sensing data processing. The convolution is a linear operation which extracts features from the input data. However, nonlinear operations are able to better characterize the internal relationships and hidden patterns within complex remote sensing data, such as hyperspectral images (HSIs). Morphological operations are powerful nonlinear transformations for feature extraction that preserve the essential characteristics of the image, such as borders, shape and structural information. In this paper, a new end-to-end morphological deep learning framework (called MorphConvHyperNet) is introduced. The proposed approach efficiently models nonlinear information during the training process of HSI classification. Specifically our method includes spectral and spatial morphological blocks to extract relevant features from the HSI input data. These morphological blocks consist of two basic 2D morphological operators (erosion and dilation) in the respective layers, followed by a weighted combination of the feature maps. Both layers can successfully encode the nonlinear information related to shape and size, playing an important role in classification performance. Our experimental results, obtained on five widely used HSIs, reveal that our newly proposed MorphConvHyperNet offers comparable (and even superior) performance when compared to traditional 2D and 3D

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Section: A Related Workmentioning

confidence: 99%

Morphological Convolutional Neural Networks for Hyperspectral Image Classification

Roy¹,

Mondal

Paoletti

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

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“…A new CNN architecture based on morphological filters was proposed in [44]. For the sake of convenience, the new presented architecture is called morphological CNN (Morph-CNN).…”

Section: B Deep-learning Based Methodsmentioning

confidence: 99%

“…For the sake of convenience, the new presented architecture is called morphological CNN (Morph-CNN). More specifically, an interpretable architecture of a CNN, which uses morphological filters in the convolutional layer, was investigated [44].…”

Section: B Deep-learning Based Methodsmentioning

confidence: 99%

Pattern Separation Network Based on the Hippocampus Activity for Handwritten Recognition

et al. 2020

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Reaching high accuracy in handwritten character recognition is an essential challenge since it is widely used in many fields such as signature analysis and forgery detection. Recently, deep learning has demonstrated efficiency in this field. The problem with deep learning is that it uses a vast number of parameters that require a large dataset for training. To overcome this problem, an intelligent network is proposed in this study, based on the computational function of the dentate gyrus of the brain's hippocampus. The ability to separate patterns with high overlapping is a task that is referred to as the dentate gyrus. Handwritten character images have high overlapping due to various writers' styles, or even one writer's style under different conditions. Therefore, proposing a network based on the dentate gyrus' functional computation can be useful in this field. One of the prominent features of the proposed network is employing two excitation steps and two inhibition steps, augmenting the accuracy of recognizing handwritten characters. The proposed network was evaluated with six datasets of digits and characters from five languages. Experiments on all of the used datasets showed promising results. Moreover, a comparative and detailed analysis of the proposed network with other SOM-based and deep learning methods is provided. Experimental results show a significant boost in accuracy. While the character error rate (CER) was smaller than 1.85% for all the experiments, the smallest CER of 0.6% was achieved by the MNIST dataset. Moreover, in recognizing patterns with high noise, the proposed network showed satisfactory results.

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“…As the reasons for breast cancer cannot be completely defined, it cannot be prevented. Early diagnosis can increase the chance of complete recovery, although breast cancer in most cases cannot be recognized until the advanced stage …”

Section: Introductionmentioning

confidence: 99%

Deep learning and optimization algorithms for automatic breast cancer detection

Sha

Hu²,

Rouyendegh

2020

Int J Imaging Syst Tech

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Breast cancer is caused by the abnormal and rapid growth of breast cells. An early diagnosis can ensure an easier and effective treatment. A mass in the breast is a significant early sign of breast cancer, even though differentiating the cancerous mass's tissue from normal tissue for diagnosis is a difficult task for radiologists. The development of computer-aided detection systems in recent years has led to nondestructive and efficient cancer diagnostic techniques. This paper proposes a comprehensive method to locate the cancerous region in the mammogram image. This method employs image noise reduction, optimal image segmentation based on the convolutional neural network, a grasshopper optimization algorithm, and optimized feature extraction and feature selection based on the grasshopper optimization algorithm, thereby improving precision and decreasing the computational cost. This method was applied to the Mammographic Image Analysis Society Digital Mammogram Database and Digital Database for Screening Mammography breast cancer databases and the simulation results were compared with 10 different state-ofthe-art methods to analyze the proposed system's efficiency. Final results showed that the proposed method had 96% Sensitivity, 93% Specificity, 85% PPV, 97% NPV, 92% accuracy, and better efficiency than other traditional methods in terms of Sensitivity, Specificity, PPV, NPV, and Accuracy. K E Y W O R D S breast cancer, convolutional neural networks, feature extraction, feature selection, grasshopper optimization algorithm, image classification, image segmentation

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Morphological Convolutional Neural Network Architecture for Digit Recognition

Cited by 69 publications

References 14 publications

Morphological Convolutional Neural Networks for Hyperspectral Image Classification

Morphological Convolutional Neural Networks for Hyperspectral Image Classification

Pattern Separation Network Based on the Hippocampus Activity for Handwritten Recognition

Deep learning and optimization algorithms for automatic breast cancer detection

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