title: Automated system for lung nodules classification based on wavelet feature descriptor and support vector machine.
abstract: BACKGROUND
  Lung cancer is a leading cause of death worldwide; it refers to the uncontrolled growth of abnormal cells in the lung. A computed tomography (CT) scan of the thorax is the most sensitive method for detecting cancerous lung nodules. A lung nodule is a round lesion which can be either non-cancerous or cancerous. In the CT, the lung cancer is observed as round white shadow nodules. The possibility to obtain a manually accurate interpretation from CT scans demands a big effort by the radiologist and might be a fatiguing process. Therefore, the design of a computer-aided diagnosis (CADx) system would be helpful as a second opinion tool.
  
  
  METHODS
  The stages of the proposed CADx are: a supervised extraction of the region of interest to eliminate the shape differences among CT images. The Daubechies db1, db2, and db4 wavelet transforms are computed with one and two levels of decomposition. After that, 19 features are computed from each wavelet sub-band. Then, the sub-band and attribute selection is performed. As a result, 11 features are selected and combined in pairs as inputs to the support vector machine (SVM), which is used to distinguish CT images containing cancerous nodules from those not containing nodules.
  
  
  RESULTS
  The clinical data set used for experiments consists of 45 CT scans from ELCAP and LIDC. For the training stage 61 CT images were used (36 with cancerous lung nodules and 25 without lung nodules). The system performance was tested with 45 CT scans (23 CT scans with lung nodules and 22 without nodules), different from that used for training. The results obtained show that the methodology successfully classifies cancerous nodules with a diameter from 2 mm to 30 mm. The total preciseness obtained was 82%; the sensitivity was 90.90%, whereas the specificity was 73.91%.
  
  
  CONCLUSIONS
  The CADx system presented is competitive with other literature systems in terms of sensitivity. The system reduces the complexity of classification by not performing the typical segmentation stage of most CADx systems. Additionally, the novelty of the algorithm is the use of a wavelet feature descriptor.
authors.affiliation: Departamento de Ingeniería Industrial y Manufactura, Instituto de Ingeniería y Tecnología, Universidad Autónoma de Ciudad Juárez, Av. del Charro 450 norte, Z. C. 32310, Ciudad Juárez, Chihuahua, México. hrm.mad@gmail.com.
authors.forename: Hiram
authors.initials: H
authors.lastname: Madero Orozco
authors.affiliation: Departamento de Ingeniería Industrial y Manufactura, Instituto de Ingeniería y Tecnología, Universidad Autónoma de Ciudad Juárez, Av. del Charro 450 norte, Z. C. 32310, Ciudad Juárez, Chihuahua, México. overgara@uacj.mx.
authors.forename: Osslan Osiris
authors.initials: OO
authors.lastname: Vergara Villegas
authors.affiliation: Departamento de Ingeniería Eléctrica y Computación, Instituto de Ingeniería y Tecnología, Universidad Autónoma de Ciudad Juárez, Av. del Charro 450 norte, Z. C. 32310, Ciudad Juárez, Chihuahua, México. Vianey.cruz@uacj.mx.
authors.forename: Vianey Guadalupe
authors.initials: VG
authors.lastname: Cruz Sánchez
authors.affiliation: Departamento de Ingeniería Eléctrica y Computación, Instituto de Ingeniería y Tecnología, Universidad Autónoma de Ciudad Juárez, Av. del Charro 450 norte, Z. C. 32310, Ciudad Juárez, Chihuahua, México. hochoa@uacj.mx.
authors.forename: Humberto de Jesús
authors.initials: Hde J
authors.lastname: Ochoa Domínguez
authors.affiliation: Departamento de Ingeniería Industrial y Manufactura, Instituto de Ingeniería y Tecnología, Universidad Autónoma de Ciudad Juárez, Av. del Charro 450 norte, Z. C. 32310, Ciudad Juárez, Chihuahua, México. mnandaya@uacj.mx.
authors.forename: Manuel de Jesús
authors.initials: Mde J
authors.lastname: Nandayapa Alfaro
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D001331:Automation; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D033162:Incidental Findings; D008175:Lung Neoplasms / Q000000981:diagnostic imaging; D010363:Pattern Recognition, Automated / Q000379:methods; D012372:ROC Curve; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012017:Referral and Consultation; D012680:Sensitivity and Specificity; D003074:Solitary Pulmonary Nodule / Q000145:classification / Q000000981:diagnostic imaging*; D060388:Support Vector Machine; D014057:Tomography, X-Ray Computed / Q000379:methods*; D058067:Wavelet Analysis