Modeling an effectual multi‐section You Only Look Once for enhancing lung cancer prediction

Jena, Sanjukta Rani; George, S. Thomas; Ponraj, D. Narain

doi:10.1002/ima.22584

Cited by 7 publications

(4 citation statements)

References 28 publications

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“…Introduced by Redmon et al, the YOLO model has achieved groundbreaking advancements in the field of object detection. Known for its efficiency in classifying and locating objects in a single step, the model has garnered sustained attention since the release of YOLOv3 [20][21][22], with ongoing improvements in its performance. Currently, in the field of object detection, YOLOv5 [23,24] and YOLOv7 [25] stand as the two dominant algorithms.…”

Section: The Yolo Familymentioning

confidence: 99%

SOCR‐YOLO: Small Objects Detection Algorithm in Medical Images

Liu,

Li,

Jiang

et al. 2024

Int J Imaging Syst Tech

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In the field of medical image analysis, object detection plays a crucial role by providing interpretable diagnostic information to healthcare professionals. Although current object detection models have achieved remarkable success in conventional images, their performance in detecting abnormalities in medical images has not been as satisfactory. This is primarily due to the complexity of anatomical structures in medical images, and the fact that some lesions may have subtle features, particularly in the case of early‐stage, small‐scale abnormalities. To address this challenge, we introduce SOCR‐YOLO, a novel lesion detection model with online convolutional reparameterization based on channel shuffling. First, it employs the SOCR (Shuffled Channel with Online Convolutional Re‐parameterization) module to establish a connection between feature concatenation and computational efficiency, aiming to extract more comprehensive information while reducing time consumption. Second, it incorporates the Bi‐FPN structure to achieve multiscale feature fusion. Lastly, the loss function has been optimized to improve the model training process. We evaluated two datasets, chest x‐ray (Vindr‐CXR) and brain tumor (Br35H), provided by the Kaggle competition. Experimental results show that the proposed method has outperformed several state‐of‐the‐art models, including YOLOv8, YOLO‐NAS, and RT‐DETR, in both speed and accuracy. Notably, in the context of chest x‐ray anomaly detection, SOCR‐YOLO exhibits a 1.8% enhancement in accuracy over YOLOv8 while simultaneously reducing floating‐point operations by 26.3%. Additionally, a similar 1.8% improvement in accuracy is observed in the detection of brain tumors. The results indicate the superior ability of our model to detect multiscale variations and small lesions.

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Section: The Yolo Familymentioning

confidence: 99%

SOCR‐YOLO: Small Objects Detection Algorithm in Medical Images

Liu,

Li,

Jiang

et al. 2024

Int J Imaging Syst Tech

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“…Deep Learning (DL) techniques have recently been used to solve various classification problems in robotics, sports, and medicine [6][7][8][9]. The most prevalent deep learning models [10] are Convolutional Neural Networks (CNNs), which offer a lot of potential for the classification of tasks [11].…”

Section: Introductionmentioning

confidence: 99%

Cephalopods Classification Using Fine Tuned Lightweight Transfer Learning Models

Prabha¹,

Suchitra²,

Saravanan³

2023

Intelligent Automation &Amp; Soft Computing

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Cephalopods identification is a formidable task that involves hand inspection and close observation by a malacologist. Manual observation and identification take time and are always contingent on the involvement of experts. A system is proposed to alleviate this challenge that uses transfer learning techniques to classify the cephalopods automatically. In the proposed method, only the Lightweight pre-trained networks are chosen to enable IoT in the task of cephalopod recognition. First, the efficiency of the chosen models is determined by evaluating their performance and comparing the findings. Second, the models are fine-tuned by adding dense layers and tweaking hyperparameters to improve the classification of accuracy. The models also employ a well-tuned Rectified Adam optimizer to increase the accuracy rates. Third, Adam with Gradient Centralisation (RAdamGC) is proposed and used in fine-tuned models to reduce the training time. The framework enables an Internet of Things (IoT) or embedded device to perform the classification tasks by embedding a suitable lightweight pre-trained network. The fine-tuned models, MobileNetV2, InceptionV3, and NASNet Mobile have achieved a classification accuracy of 89.74%, 87.12%, and 89.74%, respectively. The findings have indicated that the fine-tuned models can classify different kinds of cephalopods. The results have also demonstrated that there is a significant reduction in the training time with RAdamGC.

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“…Furthermore, deep learning has allowed researchers and clinicians to evaluate large amounts of data to forecast the propagation of COVID-19, running as an early warning mechanism for potential pandemics and classifying vulnerable populations. AI addresses and predicts new diseases by understanding infection rates, helping to provide a fully automatic and quick diagnosis for COVID-19 from X-ray images [11], and evaluates the prediction performance of death by COVID-19 based on the demographic and clinical factors [12], among others [13]. In this sense, to address the COVID-19 pandemic, AI's intrinsic benefits are being harnessed [14].…”

Section: Introductionmentioning

confidence: 99%

Towards an Approach for Filtration Efficiency Estimation of Consumer-Grade Face Masks Using Thermography

et al. 2022

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Due to the increasing need for continuous use of face masks caused by COVID-19, it is essential to evaluate the filtration quality that each face mask provides. In this research, an estimation method based on thermal image processing was developed; the main objective was to evaluate the effectiveness of different face masks while being used during breathing. For the acquisition of heat distribution images, a thermographic imaging system was built; moreover, a deep learning model detected the leakage percentage of each face mask with a mAP of 0.9345, recall of 0.842 and F1-score of 0.82. The results obtained from this research revealed that the filtration effectiveness depended on heat loss through the manufacturing material; the proposed estimation method is simple, fast, and can be replicated and operated by people who are not experts in the computer field.

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Modeling an effectual multi‐section You Only Look Once for enhancing lung cancer prediction

Cited by 7 publications

References 28 publications

SOCR‐YOLO: Small Objects Detection Algorithm in Medical Images

SOCR‐YOLO: Small Objects Detection Algorithm in Medical Images

Cephalopods Classification Using Fine Tuned Lightweight Transfer Learning Models

Towards an Approach for Filtration Efficiency Estimation of Consumer-Grade Face Masks Using Thermography

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