A Novel Efficient Video Smoke Detection Algorithm Using Co-occurrence of Local Binary Pattern Variants

Prema, C. Emmy; Suresh, S.; Murugesan, K.; Leema, N.

doi:10.1007/s10694-022-01306-2

Cited by 66 publications

(6 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Section: Training and Validation Curvementioning

confidence: 93%

“…Table 2 also offered information on each of the five pre-trained models [20,21]. In DenseNet, the input image is convoluted numerous times to provide high-level features.…”

Section: Pneumonia Prediction Using Pre-trained Modelsmentioning

confidence: 99%

“…The value of validation accuracy shows a sudden increase from 10% to 88% and then shows a stable increase in value after that. The training accuracy's value is above 90% whereas its value was constant from an epoch value of 15 to an epoch value of20.Figure5cexhibits the training and validation accuracy for the SGD optimizer. The figure indicates that the value of validation accuracy is continuously increasing from an epoch value of 5 to an epoch value of 30.…”

mentioning

confidence: 96%

See 2 more Smart Citations

Detection of Pneumonia from Chest X-ray Images Utilizing MobileNet Model

et al. 2023

View full text Add to dashboard Cite

Pneumonia has been directly responsible for a huge number of deaths all across the globe. Pneumonia shares visual features with other respiratory diseases, such as tuberculosis, which can make it difficult to distinguish between them. Moreover, there is significant variability in the way chest X-ray images are acquired and processed, which can impact the quality and consistency of the images. This can make it challenging to develop robust algorithms that can accurately identify pneumonia in all types of images. Hence, there is a need to develop robust, data-driven algorithms that are trained on large, high-quality datasets and validated using a range of imaging techniques and expert radiological analysis. In this research, a deep-learning-based model is demonstrated for differentiating between normal and severe cases of pneumonia. This complete proposed system has a total of eight pre-trained models, namely, ResNet50, ResNet152V2, DenseNet121, DenseNet201, Xception, VGG16, EfficientNet, and MobileNet. These eight pre-trained models were simulated on two datasets having 5856 images and 112,120 images of chest X-rays. The best accuracy is obtained on the MobileNet model with values of 94.23% and 93.75% on two different datasets. Key hyperparameters including batch sizes, number of epochs, and different optimizers have all been considered during comparative interpretation of these models to determine the most appropriate model.

show abstract

Section: Training and Validation Curvementioning

confidence: 93%

“…Table 2 also offered information on each of the five pre-trained models [20,21]. In DenseNet, the input image is convoluted numerous times to provide high-level features.…”

Section: Pneumonia Prediction Using Pre-trained Modelsmentioning

confidence: 99%

mentioning

confidence: 96%

See 1 more Smart Citation

Detection of Pneumonia from Chest X-ray Images Utilizing MobileNet Model

et al. 2023

View full text Add to dashboard Cite

show abstract

“…It fine-tunes a previously learned model on a smaller, similar dataset [27]. The pre-trained model already understands the input features; it needs less data to train a model [28]. Transfer learning enhances model generalization by training applicable qualities across tasks.…”

Section: Transfer Learning Modelmentioning

confidence: 99%

A Convolution Neural Network Design for Knee Osteoarthritis Diagnosis Using X-ray Images

Almansour,

Singh,

Alyami

et al. 2023

Int. J. Onl. Eng.

View full text Add to dashboard Cite

Knee osteoarthritis (OA) is a chronic degenerative joint disease affecting millions worldwide, particularly those over 60. It is a significant cause of disability and can impact an individual's quality of life. The condition occurs when the cartilage in the knee joint wears away over time, leading to bone-on-bone contact, which can result in pain, stiffness, swelling, and decreased range of motion. Deep neural networks, especially convolutional neural networks (CNN), are powerful tools in medical applications such as diagnosis and detection. This research proposes a CNN model to classify knee osteoarthritis into five categories using x-ray images. These classes are labeled: Minimal, Healthy, Moderate, Doubtful, and Severe. Furthermore, the proposed CNN model has been compared with two pre-trained transfer learning models: Xception and InceptionResNet V2. These models were evaluated based on precision, recall, F1 score, and accuracy. The results showed that although all three models performed very well, the proposed model outperformed both transfer learning models with 98% accuracy. It also achieved the highest values for other parameters such as precision, recall, and F1 score. The proposed model has several potential applications in clinical practice, such as assisting doctors in accurately classifying knee osteoarthritis severity levels by analyzing single X-ray images.

show abstract

“…Deep learning is a solution to this problem, which can automatically segment healthy organs to speed up the treatment. In medical imaging, deep learning algorithms may segment or identify specific structures or regions of interest within an image [7][8][9]. The segmentation method requires training a model on a vast dataset of annotated images, where the GI tract is designated as a distinct region of interest [10].…”

Section: Introductionmentioning

confidence: 99%

Semantic Segmentation of Gastrointestinal Tract in MRI Scans Using PSPNet Model With ResNet34 Feature Encoding Network

Sharma,

Gupta,

Rajab

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Gastrointestinal (GI) cancer is the most common cancer in men and women. GI cancers are increasing every year worldwide. In the biomedical industry, Radiation treatment is a frequent choice for treating cancers of the GI tract in which the oncologist focuses the high range of X-ray beams on the tumor while avoiding the healthy organs. Manual segmentation of healthy organs to focus X-ray beams only on the tumor portion is very tedious and time-consuming, which can lead the treatment from a few minutes to hours. Deep learning techniques can concur with this problem by segmentation of healthy organs. This research article proposes a deep learning-based model Pyramid Scene Parsing Network (PSPNet) for segmenting organs such as the stomach, small bowel, and large bowel in the GI tract. The model has been simulated with five feature encoding networks: ResNext 50, Timm_Gernet_S, ResNet 34, EfficientNet B1, and MobileNet V2. These encoders were used for downsampling the feature map in the PSPNet architecture. The implementations have been performed using the UW Madison GI tract dataset, which contains 38,496 MRI scans of cancer patients. The model was evaluated using validation dice, Jaccard, and validation loss. The results reveal that the PSPNet model combined with ResNet 34 as encoder outperforms the other feature encoding networks with validation dice as 0.8842, validation Jaccard as 0.8531, and validation loss as 0.1365. Radiation oncologists can use the proposed model to speed up radiation therapy for cancer treatment.

show abstract

A Novel Efficient Video Smoke Detection Algorithm Using Co-occurrence of Local Binary Pattern Variants

Cited by 66 publications

References 28 publications

Detection of Pneumonia from Chest X-ray Images Utilizing MobileNet Model

Detection of Pneumonia from Chest X-ray Images Utilizing MobileNet Model

A Convolution Neural Network Design for Knee Osteoarthritis Diagnosis Using X-ray Images

Semantic Segmentation of Gastrointestinal Tract in MRI Scans Using PSPNet Model With ResNet34 Feature Encoding Network

Contact Info

Product

Resources

About