Segmentation and Classification of Lung Cancer: A Review

Amin, Javeria; Sharif, Muhammad Nawaz; Yasmin, Mussarat

doi:10.2174/187152221602161221215304

Cited by 9 publications

(4 citation statements)

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“…There has been plenty of work carried out in the area of KOA imaging to identify and classify knee diseases. In image processing, feature extraction is an effective step for image representation [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 ]. For the recognition of diseases, feature extraction is very helpful to machine learning (ML) algorithms.…”

Section: Related Workmentioning

confidence: 99%

Recognition of Knee Osteoarthritis (KOA) Using YOLOv2 and Classification Based on Convolutional Neural Network

Yunus

Amin

Sharif

et al. 2022

Life

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Knee osteoarthritis (KOA) is one of the deadliest forms of arthritis. If not treated at an early stage, it may lead to knee replacement. That is why early diagnosis of KOA is necessary for better treatment. Manually KOA detection is a time-consuming and error-prone task. Computerized methods play a vital role in accurate and speedy detection. Therefore, the classification and localization of the KOA method are proposed in this work using radiographic images. The two-dimensional radiograph images are converted into three-dimensional and LBP features are extracted having the dimension of N × 59 out of which the best features of N × 55 are selected using PCA. The deep features are also extracted using Alex-Net and Dark-net-53 with the dimensions of N × 1024 and N × 4096, respectively, where N represents the number of images. Then, N × 1000 features are selected individually from both models using PCA. Finally, the extracted features are fused serially with the dimension of N × 2055 and passed to the classifiers on a 10-fold cross-validation that provides an accuracy of 90.6% for the classification of KOA grades. The localization model is proposed with the combination of an open exchange neural network (ONNX) and YOLOv2 that is trained on the selected hyper-parameters. The proposed model provides 0.98 mAP for the localization of classified images. The experimental analysis proves that the presented framework provides better results as compared to existing works.

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Section: Related Workmentioning

confidence: 99%

Recognition of Knee Osteoarthritis (KOA) Using YOLOv2 and Classification Based on Convolutional Neural Network

Yunus

Amin

Sharif

et al. 2022

Life

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“…However, the training datasets used in these methods needed to be more balanced regarding class distribution, which reduced accuracy in both detection and classification tasks. This is due to the unequal number of training data for each class in the dataset 13–15 . Recently, validating the huge amount of MRI and predicting brain tumor by a human manually this is a tedious and inappropriate task.…”

Section: Introductionmentioning

confidence: 99%

“…This is due to the unequal number of training data for each class in the dataset. [13][14][15] Recently, validating the huge amount of MRI and predicting brain tumor by a human manually this is a tedious and inappropriate task. It can influence the appropriate medical treatment of patient.…”

Section: Introductionmentioning

confidence: 99%

Designing a deep hybridized residual and SE model for MRI image‐based brain tumor prediction

Saran Raj,

Surendiran,

Raja

2024

J of Clinical Ultrasound

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Deep learning techniques have become crucial in the detection of brain tumors but classifying numerous images is time‐consuming and error‐prone, impacting timely diagnosis. This can hinder the effectiveness of these techniques in detecting brain tumors in a timely manner. To address this limitation, this study introduces a novel brain tumor detection system. The main objective is to overcome the challenges associated with acquiring a large and well‐classified dataset. The proposed approach involves generating synthetic Magnetic Resonance Imaging (MRI) images that mimic the patterns commonly found in brain MRI images. The system utilizes a dataset consisting of small images that are unbalanced in terms of class distribution. To enhance the accuracy of tumor detection, two deep learning models are employed. Using a hybrid ResNet+SE model, we capture feature distributions within unbalanced classes, creating a more balanced dataset. The second model, a tailored classifier identifies brain tumors in MRI images. The proposed method has shown promising results, achieving a high detection accuracy of 98.79%. This highlights the potential of the model as an efficient and cost‐effective system for brain tumor detection.

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“…Lung cancer causes almost 1.3 million mortalities/year globally (1). Cigarette smoke and radon exposure are the major risk factors for lung cancer (2).…”

Section: Introductionmentioning

confidence: 99%

miR‑19 in blood plasma reflects lung cancer occurrence but is not specifically associated with radon exposure

et al. 2018

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Radon is one of the most powerful carcinogens, particularly in terms of lung cancer onset and development. miRNAs may be considered not only as markers of the ongoing tumorigenesis but also as a hallmark of exposure to radiation, including radon and its progeny. Therefore, the purpose of the present study was to estimate the value of plasma miR-19b-3p level as the prospective marker of the response to radon exposure in lung cancer pathogenesis. A total of 136 subjects were examined, including 49 radon-exposed patients with lung cancer, 37 patients with lung cancer without radon exposure and 50 age/sex matched healthy controls. Total RNA from blood samples was extracted and used to detect miR-19b-3p expression via reverse transcription quantitative-polymerase chain reaction. The 2 method was used to quantify the amount of relative miRNA. The plasma level of p53 protein was determined using a Human p53 ELISA kit. Plasma miR-19b-3p level was significantly higher in the patients with lung cancer groups, compared with the healthy control group (P<0.0001). No other statistically significant differences were determined in the expression level of plasma miR-19b-3p between patients diagnosed with lung cancer exposed to radon and not exposed to radon. The expression level of free circulating miR-19b-3p was higher in the group of non-smoking patients with lung cancer, compared with smokers with lung cancer. The miR-19b-3p was 1.4-fold higher in non-smokers than in smokers (P<0.05). No association between plasma levels of p53 protein and miR-19b-3p freely circulating in patients with lung cancer was observed. No other statistically significant differences were determined in the plasma p53 protein level between patients diagnosed with lung cancer exposed and not exposed to radon. These results indicated that detection of miR-19b-3p levels in plasma potentially could be exploited as a noninvasive method for the lung cancer diagnostics. However, this miRNA is not suitable as the precise marker for radon impact.

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Segmentation and Classification of Lung Cancer: A Review

Cited by 9 publications

References 0 publications

Recognition of Knee Osteoarthritis (KOA) Using YOLOv2 and Classification Based on Convolutional Neural Network

Recognition of Knee Osteoarthritis (KOA) Using YOLOv2 and Classification Based on Convolutional Neural Network

Designing a deep hybridized residual and SE model for MRI image‐based brain tumor prediction

miR‑19 in blood plasma reflects lung cancer occurrence but is not specifically associated with radon exposure

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