An Empirical Analysis of an Optimized Pretrained Deep Learning Model for COVID-19 Diagnosis

Sangeetha, S.; Kumar, M. Sandeep; Deeba, K; Rajadurai, Hariharan; Maheshwari, V.; Dalu, Gemmachis Teshite

doi:10.1155/2022/9771212

Cited by 13 publications

(9 citation statements)

References 33 publications

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“…Performance in terms of brain tumor image classification was compared using accuracy. An epoch comparison of the existing inbuilt models [29] and the proposed model from show that using MCTL can improve the accuracy by 1.5%, thereby indicating that MCTL facilitates the detection of small targets shown in Figure 12.…”

Section: Resultsmentioning

confidence: 92%

Multiconvolutional Transfer Learning for 3D Brain Tumor Magnetic Resonance Images

Sangeetha

Muthukumaran

Deeba

et al. 2022

Computational Intelligence and Neuroscience

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The difficulty or cost of obtaining data or labels in applications like medical imaging has progressed less quickly. If deep learning techniques can be implemented reliably, automated workflows and more sophisticated analysis may be possible in previously unexplored areas of medical imaging. In addition, numerous characteristics of medical images, such as their high resolution, three-dimensional nature, and anatomical detail across multiple size scales, can increase the complexity of their analysis. This study employs multiconvolutional transfer learning (MCTL) for applying deep learning to small medical imaging datasets in an effort to address these issues. Multiconvolutional transfer learning is a model based on transfer learning that enables deep learning with small datasets. In order to learn new features on a smaller target dataset, an initial baseline is used in the transfer learning process. In this study, 3D MRI images of brain tumors are classified using a convolutional autoencoder method. In order to use unenhanced Magnetic Resonance Imaging (MRI) for clinical diagnosis, expensive and invasive contrast-enhancing procedures must be performed. MCTL has been shown to increase accuracy by 1.5%, indicating that small targets are more easily detected with MCTL. This research can be applied to a wide range of medical imaging and diagnostic procedures, including improving the accuracy of brain tumor severity diagnosis through the use of MRI.

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Section: Resultsmentioning

confidence: 92%

Multiconvolutional Transfer Learning for 3D Brain Tumor Magnetic Resonance Images

Sangeetha

Muthukumaran

Deeba

et al. 2022

Computational Intelligence and Neuroscience

Self Cite

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“…The Naive Bayes algorithm adopts the Bayes theorem and works under the assumption that attributes are unrelated. Even when other variables are available, it is impossible to know anything about other aspects [ 34 ]. So, the augmented dataset is applied to this algorithm to detect arrhythmia types without explicitly knowing about other parameters or attributes present in the dataset.…”

Section: Proposed Systemmentioning

confidence: 99%

Investigation of Applying Machine Learning and Hyperparameter Tuned Deep Learning Approaches for Arrhythmia Detection in ECG Images

Kogilavani¹,

Sathishkumar

Kumar

et al. 2022

Computational and Mathematical Methods in Medicine

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The level of patient’s illness is determined by diagnosing the problem through different methods like physically examining patients, lab test data, and history of patient and by experience. To treat the patient, proper diagnosis is very much important. Arrhythmias are irregular variations in normal heart rhythm, and detecting them manually takes a long time and relies on clinical skill. Currently machine learning and deep learning models are used to automate the diagnosis by capturing unseen patterns from datasets. This research work concentrates on data expansion using augmentation technique which increases the dataset size by generating different images. The proposed system develops a medical diagnosis system which can be used to classify arrhythmia into different categories. Initially, machine learning techniques like Support Vector Machine (SVM), Naïve Bayes (NB), and Logistic Regression (LR) are used for diagnosis. In general deep learning models are used to extract high level features and to provide improved performance over machine learning algorithms. In order to achieve this, the proposed system utilizes a deep learning algorithm known as Convolutional Neural Network-baseline model for arrhythmia detection. The proposed system also adopts a novel hyperparameter tuned CNN model to acquire optimal combination of parameters that minimizes loss function and produces better result. The result shows that the hyper-tuned model outperforms other machine learning models and CNN baseline model for accurate classification of normal and other five different arrhythmia types.

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“…Tis is possible by amalgamating microwaves and infrared thermography using a Convolutional Neural Network (CNN). Microwaves and infrared thermography are used as [9,10] sources of radiation and heat imaging recorders, respectively. By transmitting the radiant energy to the mammary gland with the electrical properties of normal and abnormal tissue, the magnetic permeability of normal tissue contradicts with the ill one, which has a radius of 5 mm, and its location can be found by placing a sensitive screen below the mamma.…”

Section: Related Workmentioning

confidence: 99%

A Novel Approach for Hybrid Image Segmentation GCPSO: FCM Techniques for MRI Brain Tumour Identification and Classification

Kavitha

Jayagopal

Kumar

et al. 2022

Computational Intelligence and Neuroscience

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In recent times, the early detection of brain tumour analysis and classification has become a very vital part of the medical field. The MRI scan image is the most significant tool to study brain tissue for proper diagnosis and efficient treatment planning to detect the early stages. In this research study, the two contributions were executed in the preprocessing mode. (a) Using wavelet transform to apply decomposed sub-bands of a low-frequency signal to control and adapt the spatial and intensity parameters in a bilateral filter and (b) to detect texture regions and block boundary to control and adapt the spatial and intensity parameters in a bilateral filter When compared to other image resolution methods, the adaptive bilateral method restores the original image quality and has a higher accuracy rate. Using the hybrid segmentation method of GCPSO (Guaranteed Convergence Particle Swarm Optimization) -FCM (Fuzzy C-Mean) techniques, the results were compared with various segmentation. The proposed segmentation gives a better accuracy rate of 95.32%.

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An Empirical Analysis of an Optimized Pretrained Deep Learning Model for COVID-19 Diagnosis

Cited by 13 publications

References 33 publications

Multiconvolutional Transfer Learning for 3D Brain Tumor Magnetic Resonance Images

Multiconvolutional Transfer Learning for 3D Brain Tumor Magnetic Resonance Images

Investigation of Applying Machine Learning and Hyperparameter Tuned Deep Learning Approaches for Arrhythmia Detection in ECG Images

A Novel Approach for Hybrid Image Segmentation GCPSO: FCM Techniques for MRI Brain Tumour Identification and Classification

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