Classification of Pulmonary Nodules by Using Hybrid Features

Tartar, Ahmet; Kılıç, Niyazi; Akan, Aydın

doi:10.1155/2013/148363

Cited by 32 publications

(16 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…That is why automatic diagnosis of breast cancer is investigated by many researchers. Computer aided diagnostic tools are intended to help physicians in order to improve the accuracy of the diagnosis [ 3 – 5 ].…”

Section: Introductionmentioning

confidence: 99%

Breast Cancer Detection with Reduced Feature Set

Mert

Kılıç

Bilgili

et al. 2015

Computational and Mathematical Methods in Medicine

Self Cite

View full text Add to dashboard Cite

This paper explores feature reduction properties of independent component analysis (ICA) on breast cancer decision support system. Wisconsin diagnostic breast cancer (WDBC) dataset is reduced to one-dimensional feature vector computing an independent component (IC). The original data with 30 features and reduced one feature (IC) are used to evaluate diagnostic accuracy of the classifiers such as k-nearest neighbor (k-NN), artificial neural network (ANN), radial basis function neural network (RBFNN), and support vector machine (SVM). The comparison of the proposed classification using the IC with original feature set is also tested on different validation (5/10-fold cross-validations) and partitioning (20%–40%) methods. These classifiers are evaluated how to effectively categorize tumors as benign and malignant in terms of specificity, sensitivity, accuracy, F-score, Youden's index, discriminant power, and the receiver operating characteristic (ROC) curve with its criterion values including area under curve (AUC) and 95% confidential interval (CI). This represents an improvement in diagnostic decision support system, while reducing computational complexity.

show abstract

Section: Introductionmentioning

confidence: 99%

Breast Cancer Detection with Reduced Feature Set

Mert

Kılıç

Bilgili

et al. 2015

Computational and Mathematical Methods in Medicine

Self Cite

View full text Add to dashboard Cite

show abstract

“…The system achieved a reliability index of 84% but was evaluated using a private dataset of only 38 scans with nodules. Tartar et al 76 detected pulmonary nodules using hybrid features: a total of 30 intensity-based and geometrical (2-D and 3-D) features were extracted and given as input to four different classifiers. Their system achieved a sensitivity of 89.6% but was evaluated using a private dataset comprising only 95 pulmonary nodules.…”

Section: False-positive Reductionmentioning

confidence: 99%

Computer-aided detection of lung nodules: a review

2019

View full text Add to dashboard Cite

We present an in-depth review and analysis of salient methods for computer-aided detection of lung nodules. We evaluate the current methods for detecting lung nodules using literature searches with selection criteria based on validation dataset types, nodule sizes, numbers of cases, types of nodules, extracted features in traditional feature-based classifiers, sensitivity, and false positives (FP)/scans. Our review shows that current detection systems are often optimized for particular datasets and can detect only one or two types of nodules. We conclude that, in addition to achieving high sensitivity and reduced FP/scans, strategies for detecting lung nodules must detect a variety of nodules with high precision to improve the performances of the radiologists. To the best of our knowledge, ours is the first review of the effectiveness of feature extraction using traditional feature-based classifiers. Moreover, we discuss deep-learning methods in detail and conclude that features must be appropriately selected to improve the overall accuracy of the system. We present an analysis of current schemes and highlight constraints and future research areas.

show abstract

“…Literatürde sıklıkla, pulmoner nodüllerin tespitine yönelik BDT sistemlerinin geliştirilmesi üzerine birçok çalışma bulunmaktadır [15,16,17].…”

Section: Sonuçlarunclassified

Malignant-benign classification of pulmonary nodules by bagging-decision trees

Tartar¹,

Akan

2015

2015 Medical Technologies National Conference (TIPTEKNO)

Self Cite

View full text Add to dashboard Cite

Özetçe Günümüzde birçok klinik uygulamada, Bilgisayar Destekli Teşhis (BDT) sistemlerinin kullanımına büyük gereksinim duyulmaktadır. Bu çalışmada, pulmoner nodüllerin malign benign sınıflandırması için yeni bir Bilgisayar Destekli Teşhis (BDT) sistemi önerilmiştir. Çalışmada bagging destekli karar ağacı sınıflandırıcıları kullanılmıştır. Malign ve benign huylu nodüllerin sınıflandırması için önerilen sistemde, % 86.8 sınıflandırma doğruluğu olmak üzere benign sınıf için % 94.7 duyarlılık ve 0.950 AUROC; malign sınıf için % 80.0 duyarlılık ve 0.888 AUROC; takibi gereken belirsiz sınıf için % 77.8 duyarlılık ve 0.935 AUROC olarak performans değerleri hesaplanmıştır. Anahtar Kelimeler -Bilgisayar destekli teşhis sistemi; Malign-benign ayrımı; yapay zeka uygulaması. Abstract Today, computer-aided detection systems have been highly needed in many clinical applications. In this study, a new Computer-aided Diagnosis system (CAD) was proposed for classifying pulmonary nodules as malignant and benign. The classifiers of the Bagging-decision trees were utilized. On the classifying of malign and benign nodule patterns, classification performance values are calculated as 94.7 % sensitivity and 0.950 AUROC for benign class; 80.0 % sensitivity and 0.888 AUROC for malign class; 77.8 % sensitivity and 0.935 AUROC for uncertain class by 86.8 % accuracy of the classifier.

show abstract

Classification of Pulmonary Nodules by Using Hybrid Features

Cited by 32 publications

References 42 publications

Breast Cancer Detection with Reduced Feature Set

Breast Cancer Detection with Reduced Feature Set

Computer-aided detection of lung nodules: a review

Malignant-benign classification of pulmonary nodules by bagging-decision trees

Contact Info

Product

Resources

About