Deep Convolution Neural Network for Big Data Medical Image Classification

Ashraf, Rehan; Habib, Muhammad Asif; Akram, Muhammad; Latif, Muhammad Ahsan; Malik, Muhammad Sheraz Arshad; Awais, Muhammad; Dar, Saadat Hanif; Mahmood, Toqeer; Yasir, Muhammad; Abbas, Zahoor

doi:10.1109/access.2020.2998808

Cited by 58 publications

(17 citation statements)

References 52 publications

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“…A data augmentation approach was used with a 10-fold augmentation system, which included cropping and flipping methods. Ashraf et al ( 13 ) attempted to classify different medical images for several body organs by employing a fine-tuning scheme to a pre-trained deep CNN model. The authors generated a combined dataset of 12 classes of human body organs (e.g., chest, breast, colon, etc.)…”

Section: Literature Reviewmentioning

confidence: 99%

MNet-10: A robust shallow convolutional neural network model performing ablation study on medical images assessing the effectiveness of applying optimal data augmentation technique

et al. 2022

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Interpretation of medical images with a computer-aided diagnosis (CAD) system is arduous because of the complex structure of cancerous lesions in different imaging modalities, high degree of resemblance between inter-classes, presence of dissimilar characteristics in intra-classes, scarcity of medical data, and presence of artifacts and noises. In this study, these challenges are addressed by developing a shallow convolutional neural network (CNN) model with optimal configuration performing ablation study by altering layer structure and hyper-parameters and utilizing a suitable augmentation technique. Eight medical datasets with different modalities are investigated where the proposed model, named MNet-10, with low computational complexity is able to yield optimal performance across all datasets. The impact of photometric and geometric augmentation techniques on different datasets is also evaluated. We selected the mammogram dataset to proceed with the ablation study for being one of the most challenging imaging modalities. Before generating the model, the dataset is augmented using the two approaches. A base CNN model is constructed first and applied to both the augmented and non-augmented mammogram datasets where the highest accuracy is obtained with the photometric dataset. Therefore, the architecture and hyper-parameters of the model are determined by performing an ablation study on the base model using the mammogram photometric dataset. Afterward, the robustness of the network and the impact of different augmentation techniques are assessed by training the model with the rest of the seven datasets. We obtain a test accuracy of 97.34% on the mammogram, 98.43% on the skin cancer, 99.54% on the brain tumor magnetic resonance imaging (MRI), 97.29% on the COVID chest X-ray, 96.31% on the tympanic membrane, 99.82% on the chest computed tomography (CT) scan, and 98.75% on the breast cancer ultrasound datasets by photometric augmentation and 96.76% on the breast cancer microscopic biopsy dataset by geometric augmentation. Moreover, some elastic deformation augmentation methods are explored with the proposed model using all the datasets to evaluate their effectiveness. Finally, VGG16, InceptionV3, and ResNet50 were trained on the best-performing augmented datasets, and their performance consistency was compared with that of the MNet-10 model. The findings may aid future researchers in medical data analysis involving ablation studies and augmentation techniques.

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Section: Literature Reviewmentioning

confidence: 99%

MNet-10: A robust shallow convolutional neural network model performing ablation study on medical images assessing the effectiveness of applying optimal data augmentation technique

et al. 2022

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“…Ahraf et al [24] (i) However, colorectal disorders are classified using convolutional neural networks.…”

Section: Attallah Andmentioning

confidence: 99%

Dexterous Identification of Carcinoma through ColoRectalCADx with Dichotomous Fusion CNN and UNet Semantic Segmentation

Raju

Jayavel

Rajalakshmi

2022

Computational Intelligence and Neuroscience

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Human colorectal disorders in the digestive tract are recognized by reference colonoscopy. The current system recognizes cancer through a three-stage system that utilizes two sets of colonoscopy data. However, identifying polyps by visualization has not been addressed. The proposed system is a five-stage system called ColoRectalCADx, which provides three publicly accessible datasets as input data for cancer detection. The three main datasets are CVC Clinic DB, Kvasir2, and Hyper Kvasir. After the image preprocessing stages, system experiments were performed with the seven prominent convolutional neural networks (CNNs) (end-to-end) and nine fusion CNN models to extract the spatial features. Afterwards, the end-to-end CNN and fusion features are executed. These features are derived from Discrete Wavelet Transform (DWT) and Vector Support Machine (SVM) classification, that was used to retrieve time and spatial frequency features. Experimentally, the results were obtained for five stages. For each of the three datasets, from stage 1 to stage 3, end-to-end CNN, DenseNet-201 obtained the best testing accuracy (98%, 87%, 84%), ((98%, 97%), (87%, 87%), (84%, 84%)), ((99.03%, 99%), (88.45%, 88%), (83.61%, 84%)). For each of the three datasets, from stage 2, CNN DaRD-22 fusion obtained the optimal test accuracy ((93%, 97%) (82%, 84%), (69%, 57%)). And for stage 4, ADaRDEV2-22 fusion achieved the best test accuracy ((95.73%, 94%), (81.20%, 81%), (72.56%, 58%)). For the input image segmentation datasets CVC Clinc-Seg, KvasirSeg, and Hyper Kvasir, malignant polyps were identified with the UNet CNN model. Here, the loss score datasets (CVC clinic DB was 0.7842, Kvasir2 was 0.6977, and Hyper Kvasir was 0.6910) were obtained.

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“…It can learn from unstructured data, likewise by utilizing fine-tuning that is done by the backpropagation approach. Deep learning might be utilized to discover features automatically from the provided dataset for each particular application [ 7 , 8 ]. Inspired by the effective and reliable execution of deep learning systems, DCNN was implemented for MRI medical images classification to classify ACL ligament tears.…”

Section: Introductionmentioning

confidence: 99%

A Torn ACL Mapping in Knee MRI Images Using Deep Convolution Neural Network with Inception-v3

Sridhar

Amutharaj

Valsalan

et al. 2022

Journal of Healthcare Engineering

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The anterior cruciate ligaments (ACL) are the fundamental structures in preserving the common biomechanics of the knees and most frequently damaged knee ligaments. An ACL injury is a tear or sprain of the ACL, one of the fundamental ligaments in the knee. ACL damage most generally happens during sports, for example, soccer, ball, football, and downhill skiing, which include sudden stops or changes in direction, jumping, and landings. Magnetic resonance imaging (MRI) has a major role in the field of diagnosis these days. Specifically, it is effective for diagnosing the cruciate ligaments and any related meniscal tears. The primary objective of this research is to detect the ACL tear from MRI knee images, which can be useful to determine the knee abnormality. In this research, a Deep Convolution Neural Network (DCNN) based Inception-v3 deep transfer learning (DTL) model was proposed for classifying the ACL tear MRI images. Preprocessing, feature extraction, and classification are the main processes performed in this research. The dataset utilized in this work was collected from the MRNet database. A total of 1,370 knee MRI images are used for evaluation. 70% of data (959 images) are used for training and testing, and 30% of data (411 images) are used in this model for performance analysis. The proposed DCNN with the Inception-v3 DTL model is evaluated and compared with existing deep learning models like VGG16, VGG19, Xception, and Inception ResNet-v28. The performance metrics like accuracy, precision, recall, specificity, and F-measure are evaluated to estimate the performance analysis of the model. The model has obtained 99.04% training accuracy and 95.42% testing accuracy in performance analysis.

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Deep Convolution Neural Network for Big Data Medical Image Classification

Cited by 58 publications

References 52 publications

MNet-10: A robust shallow convolutional neural network model performing ablation study on medical images assessing the effectiveness of applying optimal data augmentation technique

MNet-10: A robust shallow convolutional neural network model performing ablation study on medical images assessing the effectiveness of applying optimal data augmentation technique

Dexterous Identification of Carcinoma through ColoRectalCADx with Dichotomous Fusion CNN and UNet Semantic Segmentation

A Torn ACL Mapping in Knee MRI Images Using Deep Convolution Neural Network with Inception-v3

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