Abstract
In order to improve the diagnosis and classification effect of pituitary tumors, this paper combines the current common machine learning methods and classification prediction methods to improve the traditional machine learning algorithms. Moreover, this paper analyzes the feature algorithm based on the feature extraction requirements of pituitary tumor pictures and compares a variety of commonly used algorithms to select a classification algorithm suitable for the model of this paper through comparison methods. In addition, this paper carries out the calculation of the prediction algorithm and verifies the algorithm according to the actual situation. Finally, based on the neural network algorithm, this paper designs and constructs the algorithm function module and combines the actual needs of pituitary tumors to build the model and verify the performance of the model. The research results show that the model system constructed in this paper meets the expected demand.
Similar content being viewed by others
References
Smith-Bindman R, Aubin C, Bailitz J et al (2014) Ultrasonography versus computed tomography for suspected nephrolithiasis[J]. N Engl J Med 371(12):1100–1110
Aspelund G, Fingeret A, Gross E et al (2014) Ultrasonography/MRI versus CT for diagnosing appendicitis[J]. Pediatrics 133(4):586–593
Nazerian P, Vanni S, Volpicelli G et al (2014) Accuracy of point-of-care multiorgan ultrasonography for the diagnosis of pulmonary embolism[J]. Chest 145(5):950–957
Levitov A, Frankel HL, Blaivas M et al (2016) Guidelines for the appropriate use of bedside general and cardiac ultrasonography in the evaluation of critically ill patients—part II: cardiac ultrasonography[J]. Crit Care Med 44(6):1206–1227
Dhir V, Isayama H, Itoi T et al (2017) Endoscopic ultrasonography-guided biliary and pancreatic duct interventions[J]. Dig Endosc 29(4):472–485
Lord J, McMullan DJ, Eberhardt RY et al (2019) Prenatal exome sequencing analysis in fetal structural anomalies detected by ultrasonography (PAGE): a cohort study[J]. Lancet 393(10173):747–757
Liu J, Liu F, Liu Y et al (2014) Lung ultrasonography for the diagnosis of severe neonatal pneumonia[J]. Chest 146(2):383–388
Marin JR, Lewiss RE, American Academy of Pediatrics et al (2015) Point-of-care ultrasonography by pediatric emergency medicine physicians[J]. Pediatrics 135(4):e1113–e1122
Sovio U, White IR, Dacey A et al (2015) Screening for fetal growth restriction with universal third trimester ultrasonography in nulliparous women in the pregnancy outcome prediction (POP) study: a prospective cohort study [J]. Lancet 386(10008):2089–2097
Mozer P, Rouprêt M, Le Cossec C et al (2015) First round of targeted biopsies using magnetic resonance imaging/ultrasonography fusion compared with conventional transrectal ultrasonography-guided biopsies for the diagnosis of localised prostate cancer[J]. BJU Int 115(1):50–57
Zanobetti M, Scorpiniti M, Gigli C et al (2017) Point-of-care ultrasonography for evaluation of acute dyspnea in the ED[J]. Chest 151(6):1295–1301
Laursen CB, Sloth E, Lassen AT et al (2014) Point-of-care ultrasonography in patients admitted with respiratory symptoms: a single-blind, randomised controlled trial[J]. Lancet Respir Med 2(8):638–646
Kitano M, Yoshida T, Itonaga M et al (2019) Impact of endoscopic ultrasonography on diagnosis of pancreatic cancer[J]. J Gastroenterol 54(1):19–32
Bazot M, Daraï E (2017) Diagnosis of deep endometriosis: clinical examination, ultrasonography, magnetic resonance imaging, and other techniques [J]. Fertil Steril 108(6):886–894
Guirguis-Blake JM, Beil TL, Senger CA et al (2014) Ultrasonography screening for abdominal aortic aneurysms: a systematic evidence review for the US preventive services task force [J]. Ann Intern Med 160(5):321–329
Choi JH, Lee SS, Choi JH et al (2014) Long-term outcomes after endoscopic ultrasonography-guided gallbladder drainage for acute cholecystitis [J]. Endoscopy 46(08):656–661
Xie C, Cox P, Taylor N et al (2016) Ultrasonography of thyroid nodules: a pictorial review [J]. Insights Imaging 7(1):77–86
Alvarez-Sánchez MV, Jenssen C, Faiss S et al (2014) Interventional endoscopic ultrasonography: an overview of safety and complications [J]. Surg Endosc 28(3):712–734
Ohuchi N, Suzuki A, Sobue T et al (2016) Sensitivity and specificity of mammography and adjunctive ultrasonography to screen for breast cancer in the Japan strategic anti-cancer randomized trial (J-START): a randomised controlled trial [J]. Lancet 387(10016):341–348
Chou EH, Dickman E, Tsou PY et al (2015) Ultrasonography for confirmation of endotracheal tube placement: a systematic review and meta-analysis [J]. Resuscitation 90:97–103
Liu J (2014) Lung ultrasonography for the diagnosis of neonatal lung disease [J]. J Matern Fetal Neonatal Med 27(8):856–861
Flores WG, de Albuquerque PWC, Infantosi AFC (2015) Improving classification performance of breast lesions on ultrasonography [J]. Pattern Recogn 48(4):1125–1136
Liu J, Chen XX, Li XW et al (2016) Lung ultrasonography to diagnose transient tachypnea of the newborn [J]. Chest 149(5):1269–1275
Logan JK, Rais-Bahrami S, Turkbey B et al (2014) Current status of magnetic resonance imaging (MRI) and ultrasonography fusion software platforms for guidance of prostate biopsies [J]. BJU Int 114(5):641–652
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors have no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Liu, A., Xiao, Y., Wu, M. et al. Diagnosis and classification prediction model of pituitary tumor based on machine learning. Neural Comput & Applic 34, 9257–9272 (2022). https://doi.org/10.1007/s00521-021-06277-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-021-06277-z