Multi-Class Classification of Lung Diseases Using CNN Models

Min, Hong; Lee, Hongchang; Jang, Hyeon-Woong; Oh, Joonho; Choi, Seo-Youn

doi:10.3390/app11199289

Cited by 39 publications

(42 citation statements)

References 14 publications

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“…Comparing with [30], the procedures are quite similar when classifying masses and microcalcifications, but what distinguishes us is the fifth category with the normal class to discriminate false positives in the best manner. It is also notorious how annotation and data augmentation significantly improve our work.…”

Section: Discussionmentioning

confidence: 99%

“…The work of Hong et al, 2021 [30] proposes a method of classifying multiple classes of lung diseases with CNN ImageNet, applying fine-tuning with their initial weights, and feeding the network with images in * .tif format. The datasets used were the National Institutes of Health (NIH) of EE, divided into Normal, Pneumonia, and Pneumothorax, and the Cheonan Soonchunhyang University Hospital dataset that includes Tuberculosis.…”

Section: Related Workmentioning

confidence: 99%

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Classification of Breast Cancer in Mammograms with Deep Learning Adding a Fifth Class

et al. 2021

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Breast cancer is one of the diseases of most profound concern, with the most prevalence worldwide, where early detections and diagnoses play the leading role against this disease achieved through imaging techniques such as mammography. Radiologists tend to have a high false positive rate for mammography diagnoses and an accuracy of around 82%. Currently, deep learning (DL) techniques have shown promising results in the early detection of breast cancer by generating computer-aided diagnosis (CAD) systems implementing convolutional neural networks (CNNs). This work focuses on applying, evaluating, and comparing the architectures: AlexNet, GoogLeNet, Resnet50, and Vgg19 to classify breast lesions after using transfer learning with fine-tuning and training the CNN with regions extracted from the MIAS and INbreast databases. We analyzed 14 classifiers, involving 4 classes as several researches have done it before, corresponding to benign and malignant microcalcifications and masses, and as our main contribution, we also added a 5th class for the normal tissue of the mammary parenchyma increasing the correct detection; in order to evaluate the architectures with a statistical analysis based on the received operational characteristics (ROC), the area under the curve (AUC), F1 Score, accuracy, precision, sensitivity, and specificity. We generate the best results with the CNN GoogLeNet trained with five classes on a balanced database with an AUC of 99.29%, F1 Score of 91.92%, the accuracy of 91.92%, precision of 92.15%, sensitivity of 91.70%, and specificity of 97.66%, concluding that GoogLeNet is optimal as a classifier in a CAD system to deal with breast cancer.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Classification of Breast Cancer in Mammograms with Deep Learning Adding a Fifth Class

et al. 2021

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“…This model was constructed using the CNN analysis method optimized for image analysis among deep learning analysis techniques. Each submodel was developed by ne-tuning using ve different pre-trained CNN models: AlexNet, ResNet50, GoogLeNet, VGG19 which ranked rst or second in ILSVRC 29,30 , and E cientNet which showed good performance in plant classi cation [22][23][24] . In step 1, a model to determine and classify cultivars was developed using images of an entire leaf, regardless of presence or absence of disease, in three Solanaceae family crops using ve pre-trained CNN models (Fig.…”

Section: Stepwise Detection Model For Plant Diseasesmentioning

confidence: 99%

Construction of Deep Learning-Based Disease Detection Model in Plants

Jung

Song

Shin

et al. 2022

Preprint

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Accurately detecting disease occurrences of crops in early stage is essential for quality and yield of crops through the decision of an appropriate treatments. However, detection of disease needs specialized knowledge and long-term experiences in plant pathology. Thus, automated system for disease detecting in crops will play an important role in agriculture by constructing early detection system of disease. To develop this system, construction of stepwise disease detection model using images of diseased-healthy plant pairs and a CNN algorithm consisting of five pre-trained models. The disease detection model consists of three step classification models, crop classification, disease detection, and disease classification. Unknown is added into categories to generalize the model for wide application. In the validation test, the disease detection model classified crops and disease types with high accuracy (97.09%). The low accuracy of non-model crops was improved by adding these crops to the training dataset implicating expendability of the model. Our model has a potential to apply to smart farming of Solanaceae crops and will be widely used by adding more various crops as training dataset.

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“…Deep neural networks (DNNs) have been widely adopted in various fields, such as in image and character recognition and object detection [1][2][3][4][5][6][7][8][9][10]. Driven by the increasing popularity of embedded systems, such as smartphones, active research is being conducted to explore on-device deep learning in embedded systems [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

“…Used only in Yolo v5. Available online: https://github.com/ultralytics/yolov5 (accessed on 23 August 2021) 2. Available online: https://github.com/SURFZJY/EAST-caffe (accessed on 23 August 2021) 3.…”

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confidence: 99%

CitiusSynapse: A Deep Learning Framework for Embedded Systems

2021

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As embedded systems, such as smartphones with limited resources, have become increasingly popular, active research has recently been conducted on performing on-device deep learning in such systems. Therefore, in this study, we propose a deep learning framework that is specialized for embedded systems with limited resources, the operation processing structure of which differs from that of standard PCs. The proposed framework supports an OpenCL-based accelerator engine for accelerator deep learning operations in various embedded systems. Moreover, the parallel processing performance of OpenCL is maximized through an OpenCL kernel that is optimized for embedded GPUs, and the structural characteristics of embedded systems, such as unified memory. Furthermore, an on-device optimizer for optimizing the performance in on-device environments, and model converters for compatibility with conventional frameworks, are provided. The results of a performance evaluation show that the proposed on-device framework outperformed conventional methods.

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Multi-Class Classification of Lung Diseases Using CNN Models

Cited by 39 publications

References 14 publications

Classification of Breast Cancer in Mammograms with Deep Learning Adding a Fifth Class

Classification of Breast Cancer in Mammograms with Deep Learning Adding a Fifth Class

Construction of Deep Learning-Based Disease Detection Model in Plants

CitiusSynapse: A Deep Learning Framework for Embedded Systems

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