Augmentation Techniques for Sequential Clinical Data to Improve Deep Learning Prediction Techniques

Malaria is a disease activated by a type of microscopic parasite transmitted from infected female mosquito bites to humans. Malaria is a fatal disease that is endemic in many regions of the world. Quick diagnosis of this disease will be very valuable for patients, as traditional methods require tedious work for its detection. Recently, some automated methods have been proposed that exploit hand-crafted feature extraction techniques however, their accuracies are not reliable. Deep learning approaches modernize the world with their superior performance. Convolutional Neural Networks (CNN) are vastly scalable for image classification tasks that extract features through hidden layers of the model without any handcrafting. The detection of malaria-infected red blood cells from segmented microscopic blood images using convolutional neural networks can assist in quick diagnosis, and this will be useful for regions with fewer healthcare experts. The contributions of this paper are two-fold. First, we evaluate the performance of different existing deep learning models for efficient malaria detection. Second, we propose a customized CNN model that outperforms all observed deep learning models. It exploits the bilateral filtering and image augmentation techniques for highlighting features of red blood cells before training the model. Due to image augmentation techniques, the customized CNN model is generalized and avoids over-fitting. All experimental evaluations are performed on the benchmark NIH Malaria Dataset, and the results reveal that the proposed algorithm is 96.82% accurate in detecting malaria from the microscopic blood smears.

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Image Enhanced Mask R-CNN: A Deep Learning Pipeline with New Evaluation Measures for Wind Turbine Blade Defect Detection and Classification

Journal of Imaging ◽

10.3390/jimaging7030046 ◽

2021 ◽

Vol 7 (3) ◽

pp. 46

Author(s):

Jiajun Zhang ◽

Georgina Cosma ◽

Jason Watkins

Keyword(s):

Deep Learning ◽

Wind Turbine ◽

Turbine Blade ◽

Defect Detection ◽

Recognition Rate ◽

Weighted Average ◽

Wind Turbine Blade ◽

Evaluation Measures ◽

Augmentation Techniques ◽

The Mean

Demand for wind power has grown, and this has increased wind turbine blade (WTB) inspections and defect repairs. This paper empirically investigates the performance of state-of-the-art deep learning algorithms, namely, YOLOv3, YOLOv4, and Mask R-CNN for detecting and classifying defects by type. The paper proposes new performance evaluation measures suitable for defect detection tasks, and these are: Prediction Box Accuracy, Recognition Rate, and False Label Rate. Experiments were carried out using a dataset, provided by the industrial partner, that contains images from WTB inspections. Three variations of the dataset were constructed using different image augmentation settings. Results of the experiments revealed that on average, across all proposed evaluation measures, Mask R-CNN outperformed all other algorithms when transformation-based augmentations (i.e., rotation and flipping) were applied. In particular, when using the best dataset, the mean Weighted Average (mWA) values (i.e., mWA is the average of the proposed measures) achieved were: Mask R-CNN: 86.74%, YOLOv3: 70.08%, and YOLOv4: 78.28%. The paper also proposes a new defect detection pipeline, called Image Enhanced Mask R-CNN (IE Mask R-CNN), that includes the best combination of image enhancement and augmentation techniques for pre-processing the dataset, and a Mask R-CNN model tuned for the task of WTB defect detection and classification.

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Deep Learning‐Based Prediction Techniques for Medical Care: Opportunities and Challenges

Computational Analysis and Deep Learning for Medical Care ◽

10.1002/9781119785750.ch17 ◽

2021 ◽

pp. 429-461

Author(s):

S. Subasree ◽

N. K. Sakthivel

Keyword(s):

Deep Learning ◽

Medical Care ◽

Prediction Techniques

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Image Data Augmentation techniques for Deep Learning -A Mirror Review

10.1109/icrito51393.2021.9596262 ◽

2021 ◽

Author(s):

Dipen Saini ◽

Rahul Malik

Keyword(s):

Deep Learning ◽

Data Augmentation ◽

Image Data ◽

Augmentation Techniques

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P4-309: MULTIMODAL-CNN: IMPROVED ACCURACY OF MRI-BASED CLASSIFICATION OF ALZHEIMER'S DISEASE BY INCORPORATING CLINICAL DATA IN DEEP LEARNING

Alzheimer s & Dementia ◽

10.1016/j.jalz.2018.07.132 ◽

2006 ◽

Vol 14 (7S_Part_30) ◽

pp. P1574-P1574

Author(s):

Taeho Jo ◽

Kwangsik Nho ◽

Shannon L. Risacher ◽

Jingwen Yan ◽

Andrew J. Saykin

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Deep Learning ◽

Clinical Data ◽

Improved Accuracy

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Lesion Detection in Breast Tomosynthesis Using Efficient Deep Learning and Data Augmentation Techniques

10.3233/faia210150 ◽

2021 ◽

Author(s):

Loay Hassan ◽

Mohamed Abedl-Nasser ◽

Adel Saleh ◽

Domenec Puig

Keyword(s):

Breast Cancer ◽

Deep Learning ◽

Breast Lesion ◽

Data Augmentation ◽

Digital Breast Tomosynthesis ◽

Lesion Detection ◽

Detection Methods ◽

Breast Tomosynthesis ◽

Mammographic Images ◽

Augmentation Techniques

Digital breast tomosynthesis (DBT) is one of the powerful breast cancer screening technologies. DBT can improve the ability of radiologists to detect breast cancer, especially in the case of dense breasts, where it beats mammography. Although many automated methods were proposed to detect breast lesions in mammographic images, very few methods were proposed for DBT due to the unavailability of enough annotated DBT images for training object detectors. In this paper, we present fully automated deep-learning breast lesion detection methods. Specifically, we study the effectiveness of two data augmentation techniques (channel replication and channel-concatenation) with five state-of-the-art deep learning detection models. Our preliminary results on a challenging publically available DBT dataset showed that the channel-concatenation data augmentation technique can significantly improve the breast lesion detection results for deep learning-based breast lesion detectors.

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Disease classification in Solanum melongena using deep learning

Spanish Journal of Agricultural Research ◽

10.5424/sjar/2019173-14762 ◽

2019 ◽

Vol 17 (3) ◽

pp. e0204 ◽

Cited By ~ 1

Author(s):

Krishnaswamy R. Aravind ◽

Purushothaman Raja ◽

Rajendran Ashiwin ◽

Konnaiyar V. Mukesh

Keyword(s):

Deep Learning ◽

Tamil Nadu ◽

Solanum Melongena ◽

Disease Classification ◽

Healthy Plant ◽

Future Research ◽

Learning Models ◽

Cercospora Leaf Spot ◽

Before And After ◽

Augmentation Techniques

Aim of study: The application of pre-trained deep learning models, AlexNet and VGG16, for classification of five diseases (Epilachna beetle infestation, little leaf, Cercospora leaf spot, two-spotted spider mite and Tobacco Mosaic Virus (TMV)) and a healthy plant in Solanum melongena (brinjal in Asia, eggplant in USA and aubergine in UK) with images acquired from smartphones.Area of study: Images were acquired from fields located at Alangudi (Pudukkottai district), Tirumalaisamudram and Pillayarpatti (Thanjavur district) – Tamil Nadu, India.Material and methods: Most of earlier studies have been carried out with images of isolated leaf samples, whereas in this work the whole or part of the plant images were utilized for the dataset creation. Augmentation techniques were applied to the manually segmented images for increasing the dataset size. The classification capability of deep learning models was analysed before and after augmentation. A fully connected layer was added to the architecture and evaluated for its performance.Main results: The modified architecture of VGG16 trained with the augmented dataset resulted in an average validation accuracy of 96.7%. Despite the best accuracy, all the models were tested with sample images from the field and the modified VGG16 resulted in an accuracy of 93.33%.Research highlights: The findings provide a guidance for possible factors to be considered in future research relevant to the dataset creation and methodology for efficient prediction using deep learning models.

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