Meta features-based scale invariant OCR decision making using LSTM-RNN

Naseer, Asma; Zafar, Kashif

doi:10.1007/s10588-018-9265-9

Cited by 26 publications

(22 citation statements)

References 24 publications

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“…CNN has been widely used for solving different problems in different areas [ 17 , 18 ] but, for the processing of images for health applications, its performance is remarkable. A lot of research exists in which CAD-based diagnosis of diseases is proposed.…”

Section: Related Workmentioning

confidence: 99%

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Computer-Aided Brain Tumor Diagnosis: Performance Evaluation of Deep Learner CNN Using Augmented Brain MRI

Naseer

Yasir

Azhar

et al. 2021

International Journal of Biomedical Imaging

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Brain tumor is a deadly neurological disease caused by an abnormal and uncontrollable growth of cells inside the brain or skull. The mortality ratio of patients suffering from this disease is growing gradually. Analysing Magnetic Resonance Images (MRIs) manually is inadequate for efficient and accurate brain tumor diagnosis. An early diagnosis of the disease can activate a timely treatment consequently elevating the survival ratio of the patients. Modern brain imaging methodologies have augmented the detection ratio of brain tumor. In the past few years, a lot of research has been carried out for computer-aided diagnosis of human brain tumor to achieve 100% diagnosis accuracy. The focus of this research is on early diagnosis of brain tumor via Convolution Neural Network (CNN) to enhance state-of-the-art diagnosis accuracy. The proposed CNN is trained on a benchmark dataset, BR35H, containing brain tumor MRIs. The performance and sustainability of the model is evaluated on six different datasets, i.e., BMI-I, BTI, BMI-II, BTS, BMI-III, and BD-BT. To improve the performance of the model and to make it sustainable for totally unseen data, different geometric data augmentation techniques, along with statistical standardization, are employed. The proposed CNN-based CAD system for brain tumor diagnosis performs better than other systems by achieving an average accuracy of around 98.8% and a specificity of around 0.99. It also reveals 100% correct diagnosis for two brain MRI datasets, i.e., BTS and BD-BT. The performance of the proposed system is also compared with the other existing systems, and the analysis reveals that the proposed system outperforms all of them.

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Section: Related Workmentioning

confidence: 99%

“…To avoid vanishing gradient difficulty, a Rectified Linear Unit (ReLU) layer is also added after each convolution layer, as an element-wise activation function. Some other CNN layers are the input layer, the dropout layer, the output layer, and the network in network layer [ 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Computer-Aided Brain Tumor Diagnosis: Performance Evaluation of Deep Learner CNN Using Augmented Brain MRI

Naseer

Yasir

Azhar

et al. 2021

International Journal of Biomedical Imaging

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“…RNNs are suitable for temporal information classification as CNNs; however, owing to their feedback property, they can handle sequences like video frames in a better way comparatively. RNNs are computationally efficient; however, generally harder to train compared with CNN, thus requiring pre-extracted features or a CNN stage before the classification stage [20].…”

Section: Radial Bias Network 1988mentioning

confidence: 99%

An Insight for Cursive Context-Specific Printed Script Recognition

Rafique¹,

Javid²

2021

Preprint

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The greatest challenge of machine learning problems is to select suitable techniques and resources such as tools and datasets. Despite the existence of millions of speakers around the globe and the rich literary history of more than a thousand years, it is expensive to find the computational linguistic work related to Punjabi Shahmukhi script, a member of the Perso-Arabic context-specific script low-resource language family. This paper presents a deep insight into the related work with summary statistics, advocating the popularity and success of artificial neural networks and related techniques. The paper includes support from recent trends from the authentic sources based on the top-level researchers' feedback including the machine learning frameworks. A comprehensive comparison of the most popular deep learning techniques convolutional neural network and the recursive neural network has been presented for the cursive context-specific scripts of Perso-Arabic nature. The overview of the available benchmark datasets for machine learning problems, especially for the Perso-Arabic group, is added. This paper incorporates essential knowledge contents for the researchers in machine learning and natural language processing disciplines on the selection of algorithms, architectures, and resources.

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“…LSTM is another deep network that shows considerably better results due to its memory cells, equation updating mechanism, and back-propagation refinements [16,17]. The architecture of LSTM makes it one of the most promising deep networks.…”

Section: Introductionmentioning

confidence: 99%

Computer-aided COVID-19 diagnosis and a comparison of deep learners using augmented CXRs

Naseer

Tamoor

Azhar

2022

XST

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Background: Coronavirus Disease 2019 (COVID-19) is contagious, producing respiratory tract infection, caused by a newly discovered coronavirus. Its death toll is too high, and early diagnosis is the main problem nowadays. Infected people show a variety of symptoms such as fatigue, fever, tastelessness, dry cough, etc. Some other symptoms may also be manifested by radiographic visual identification. Therefore, Chest X-Rays (CXR) play a key role in the diagnosis of COVID-19. Methods: In this study, we use Chest X-Rays images to develop a computer-aided diagnosis (CAD) of the disease. These images are used to train two deep networks, the Convolution Neural Network (CNN), and the Long Short-Term Memory Network (LSTM) which is an artificial Recurrent Neural Network (RNN). The proposed study involves three phases. First, the CNN model is trained on raw CXR images. Next, it is trained on pre-processed CXR images and finally enhanced CXR images are used for deep network CNN training. Geometric transformations, color transformations, image enhancement, and noise injection techniques are used for augmentation. From augmentation, we get 3,220 augmented CXRs as training datasets. In the final phase, CNN is used to extract the features of CXR imagery that are fed to the LSTM model. The performance of the four trained models is evaluated by the evaluation techniques of different models, including accuracy, specificity, sensitivity, false-positive rate, and receiver operating characteristic (ROC) curve. Results: We compare our results with other benchmark CNN models. Our proposed CNN-LSTM model gives superior accuracy (99.02%) than the other state-of-the-art models. Our method to get improved input, helped the CNN model to produce a very high true positive rate (TPR 1) and no false-negative result whereas false negative was a major problem while using Raw CXR images. Conclusions: We conclude after performing different experiments that some image pre-processing and augmentation, remarkably improves the results of CNN-based models. It will help a better early detection of the disease that will eventually reduce the mortality rate of COVID.

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Meta features-based scale invariant OCR decision making using LSTM-RNN

Cited by 26 publications

References 24 publications

Computer-Aided Brain Tumor Diagnosis: Performance Evaluation of Deep Learner CNN Using Augmented Brain MRI

Computer-Aided Brain Tumor Diagnosis: Performance Evaluation of Deep Learner CNN Using Augmented Brain MRI

An Insight for Cursive Context-Specific Printed Script Recognition

Computer-aided COVID-19 diagnosis and a comparison of deep learners using augmented CXRs

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