Lung nodules detection using semantic segmentation and classification with optimal features

Meraj, Talha; Rauf, Hafiz Tayyab; Zahoor, Saliha; Hassan, Arslan; Lali, M. Ikramullah; Ali, Liaqat; Bukhari, Syed; Shoaib, Umar

doi:10.1007/s00521-020-04870-2

Cited by 66 publications

(27 citation statements)

References 30 publications

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“…Compared with other types of cancer, there is less study on class imbalance methods for lung cancer. Few research es als o classified the Lung nodules [83,84], chest-related diseases [85,86], identification of thoracic diseases [87], forecasting of COVID-19 [84,88,89].…”

Section: Application Of Class Imbalance M Ethods To Cancer Datasetsmentioning

confidence: 99%

A Comparative Performance Analysis of Data Resampling Methods on Imbalance Medical Data

et al. 2021

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Medical datasets are usually imbalanced, where negative cases severely outnumber p osit iv e cases. Therefore, it is essential to deal with this data skew problem when training machine learning algorithms. This study uses two representative lung cancer datasets, PLCO an d NLST, wit h imb alan ce ratios (the proportion of samples in the majority class to those in the minority class) of 24.7 and 25.0, respectively, to predict lung cancer incidence. This research uses the performance o f 23 clas s imb alan ce methods (resampling and hybrid systems) with three classical classifiers (logistic regression, random forest, and LinearSVC) to identify the best imbalance techniques suitable for medical datasets. Resampling includes ten under-sampling methods (RUS, Etc.), seven over-sampling methods (SMOTE, Etc.), an d t wo integrated sampling methods (SMOTEENN, SMOTE-Tomek). Hybrid systems include (Balanced Bagging, Etc.). The results show that class imbalance learning can improve the classification abilit y o f t h e mo d el. Compared with other imbalanced techniques, under-sampling techniques have the highest standard deviation (SD), and over-sampling techniques have the lowest SD. Over-sampling is a stable met h od, an d the AUC in the model is generally higher than in other ways. Using ROS, the random forest p erforms t h e best predictive ability and is more suitable for the lung cancer datasets used in this study.

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Section: Application Of Class Imbalance M Ethods To Cancer Datasetsmentioning

confidence: 99%

A Comparative Performance Analysis of Data Resampling Methods on Imbalance Medical Data

et al. 2021

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“…In the recent era, many studies have suggested employing deep learning as it performs well in medical imaging diagnosis and other image recognition challenges [10,20]. A new dataset of ultrasonic imaging with given label masks is used to solve segmentation and classification challenges [45].…”

Section: Methodsmentioning

confidence: 99%

“…Existing studies lack vigor, intensity inhomogeneity, artifact removal, and precise lesion segmentation [15,16,18,19]. Because of the deep convolutional process, which extracts rich feature vectors, deep learning-based approaches for semantic segmentation and classification have gained popularity [19,20].…”

Section: Introductionmentioning

confidence: 99%

Dilated Semantic Segmentation for Breast Ultrasonic Lesion Detection Using Parallel Feature Fusion

et al. 2021

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Breast cancer is becoming more dangerous by the day. The death rate in developing countries is rapidly increasing. As a result, early detection of breast cancer is critical, leading to a lower death rate. Several researchers have worked on breast cancer segmentation and classification using various imaging modalities. The ultrasonic imaging modality is one of the most cost-effective imaging techniques, with a higher sensitivity for diagnosis. The proposed study segments ultrasonic breast lesion images using a Dilated Semantic Segmentation Network (Di-CNN) combined with a morphological erosion operation. For feature extraction, we used the deep neural network DenseNet201 with transfer learning. We propose a 24-layer CNN that uses transfer learning-based feature extraction to further validate and ensure the enriched features with target intensity. To classify the nodules, the feature vectors obtained from DenseNet201 and the 24-layer CNN were fused using parallel fusion. The proposed methods were evaluated using a 10-fold cross-validation on various vector combinations. The accuracy of CNN-activated feature vectors and DenseNet201-activated feature vectors combined with the Support Vector Machine (SVM) classifier was 90.11 percent and 98.45 percent, respectively. With 98.9 percent accuracy, the fused version of the feature vector with SVM outperformed other algorithms. When compared to recent algorithms, the proposed algorithm achieves a better breast cancer diagnosis rate.

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“…AI is now mature to be embedded with the state of the art machine learning, and deep learning algorithm in different domains such as medicine (Meraj et al, 2019), agriculture (Rauf et al, 2019), biometrics (Jin, Cruz & Goncalves, 2020), renewable energy (Gao, Wang & Shen, 2020b), and cloud computing (Gao, Wang & Shen, 2020c;Gao, Wang & Shen, 2020a). AI is gradually changing medicine as we know it, as more and more ways are being researched to automate medical practices and support the doctors and specialists (Brag, 2020;Rauf et al, 2020;Kulkarni et al, 2020;Briganti & Le Moine, 2020).…”

Section: Introductionmentioning

confidence: 99%

AI-driven deep CNN approach for multi-label pathology classification using chest X-Rays

Albahli

Rauf

Algosaibi

et al. 2021

PeerJ Computer Science

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Artificial intelligence (AI) has played a significant role in image analysis and feature extraction, applied to detect and diagnose a wide range of chest-related diseases. Although several researchers have used current state-of-the-art approaches and have produced impressive chest-related clinical outcomes, specific techniques may not contribute many advantages if one type of disease is detected without the rest being identified. Those who tried to identify multiple chest-related diseases were ineffective due to insufficient data and the available data not being balanced. This research provides a significant contribution to the healthcare industry and the research community by proposing a synthetic data augmentation in three deep Convolutional Neural Networks (CNNs) architectures for the detection of 14 chest-related diseases. The employed models are DenseNet121, InceptionResNetV2, and ResNet152V2; after training and validation, an average ROC-AUC score of 0.80 was obtained competitive as compared to the previous models that were trained for multi-class classification to detect anomalies in x-ray images. This research illustrates how the proposed model practices state-of-the-art deep neural networks to classify 14 chest-related diseases with better accuracy.

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Lung nodules detection using semantic segmentation and classification with optimal features

Cited by 66 publications

References 30 publications

A Comparative Performance Analysis of Data Resampling Methods on Imbalance Medical Data

A Comparative Performance Analysis of Data Resampling Methods on Imbalance Medical Data

Dilated Semantic Segmentation for Breast Ultrasonic Lesion Detection Using Parallel Feature Fusion

AI-driven deep CNN approach for multi-label pathology classification using chest X-Rays

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