TUTCRIS - Tampereen teknillinen yliopisto

TUTCRIS

Computer aided diagnosis of acoustic neuroma: A neural network perspective

Tutkimustuotos: Katsausartikkelivertaisarvioitu

Standard

Computer aided diagnosis of acoustic neuroma : A neural network perspective. / Anwar, Shahzad; Izhar-Ul-Haq, Izhar; Qadir, Muhammad Usman; Ali, Ihtisham; Razzaq, Shadman; Ahmad, Bilal; Shah, Kamran; Shah, Shaukat Ali; Khan, Muhammad Tahir.

julkaisussa: JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS, Vuosikerta 7, Nro 2, 01.04.2017, s. 371-377.

Tutkimustuotos: Katsausartikkelivertaisarvioitu

Harvard

Anwar, S, Izhar-Ul-Haq, I, Qadir, MU, Ali, I, Razzaq, S, Ahmad, B, Shah, K, Shah, SA & Khan, MT 2017, 'Computer aided diagnosis of acoustic neuroma: A neural network perspective', JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS, Vuosikerta. 7, Nro 2, Sivut 371-377. https://doi.org/10.1166/jmihi.2017.2057

APA

Anwar, S., Izhar-Ul-Haq, I., Qadir, M. U., Ali, I., Razzaq, S., Ahmad, B., ... Khan, M. T. (2017). Computer aided diagnosis of acoustic neuroma: A neural network perspective. JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS, 7(2), 371-377. https://doi.org/10.1166/jmihi.2017.2057

Vancouver

Anwar S, Izhar-Ul-Haq I, Qadir MU, Ali I, Razzaq S, Ahmad B et al. Computer aided diagnosis of acoustic neuroma: A neural network perspective. JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS. 2017 huhti 1;7(2):371-377. https://doi.org/10.1166/jmihi.2017.2057

Author

Anwar, Shahzad ; Izhar-Ul-Haq, Izhar ; Qadir, Muhammad Usman ; Ali, Ihtisham ; Razzaq, Shadman ; Ahmad, Bilal ; Shah, Kamran ; Shah, Shaukat Ali ; Khan, Muhammad Tahir. / Computer aided diagnosis of acoustic neuroma : A neural network perspective. Julkaisussa: JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS. 2017 ; Vuosikerta 7, Nro 2. Sivut 371-377.

Bibtex - Lataa

@article{845e51890f1543a8832a325ff3b48d62,
title = "Computer aided diagnosis of acoustic neuroma: A neural network perspective",
abstract = "Acoustic neuroma is a benign brain tumour, if not identified and diagnosed at early stages can result in loss of hearing. In this study Multilayer Perceptron (MLP) a feedforward Artificial Neural Network model (ANN), has been investigated for segmentation and detection of acoustic neuroma at early stages using medical resonance imaging (MRI). The proposed methodology comprises of two phases. In first phase, regions in the MRI images were classified and segmented as affected (neuroma) or normal areas. The classification was performed on pixel level using MLP. During this phase Region of Interest (ROI) was created through domain-specific knowledge. The MLP was then trained using 1490 random samples. In the second phase acoustic neuroma was detected in MRI images. The statistical analyses were subsequently performed on the pixel level to calculate the current size of detected neuroma and to provide suggestive deadlines for treatment using the growth rate of the tumour. The methodology presented in this research is more efficient compared to state-of-The art practices. The proposed technique not only detects acoustic neuroma but also highlights its location and boundaries and estimates the size of neuroma. Moreover, the computational time associated with detection and size prediction is much less in comparison to the other existing techniques. Exceptional results were obtained with an over-All accuracy of 94.12{\%} in the detection of Acoustic Neuroma during final experimentation.",
keywords = "Acoustic Neuroma, Image Processing, Machine Vision, Magnetic Resonance Image (MRI), Multi-Layer Perceptron, Segmentation",
author = "Shahzad Anwar and Izhar Izhar-Ul-Haq and Qadir, {Muhammad Usman} and Ihtisham Ali and Shadman Razzaq and Bilal Ahmad and Kamran Shah and Shah, {Shaukat Ali} and Khan, {Muhammad Tahir}",
note = "INT=sgn,{"}Razzaq, Shadman{"}",
year = "2017",
month = "4",
day = "1",
doi = "10.1166/jmihi.2017.2057",
language = "English",
volume = "7",
pages = "371--377",
journal = "JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS",
issn = "2156-7018",
publisher = "American Scientific Publishers",
number = "2",

}

RIS (suitable for import to EndNote) - Lataa

TY - JOUR

T1 - Computer aided diagnosis of acoustic neuroma

T2 - A neural network perspective

AU - Anwar, Shahzad

AU - Izhar-Ul-Haq, Izhar

AU - Qadir, Muhammad Usman

AU - Ali, Ihtisham

AU - Razzaq, Shadman

AU - Ahmad, Bilal

AU - Shah, Kamran

AU - Shah, Shaukat Ali

AU - Khan, Muhammad Tahir

N1 - INT=sgn,"Razzaq, Shadman"

PY - 2017/4/1

Y1 - 2017/4/1

N2 - Acoustic neuroma is a benign brain tumour, if not identified and diagnosed at early stages can result in loss of hearing. In this study Multilayer Perceptron (MLP) a feedforward Artificial Neural Network model (ANN), has been investigated for segmentation and detection of acoustic neuroma at early stages using medical resonance imaging (MRI). The proposed methodology comprises of two phases. In first phase, regions in the MRI images were classified and segmented as affected (neuroma) or normal areas. The classification was performed on pixel level using MLP. During this phase Region of Interest (ROI) was created through domain-specific knowledge. The MLP was then trained using 1490 random samples. In the second phase acoustic neuroma was detected in MRI images. The statistical analyses were subsequently performed on the pixel level to calculate the current size of detected neuroma and to provide suggestive deadlines for treatment using the growth rate of the tumour. The methodology presented in this research is more efficient compared to state-of-The art practices. The proposed technique not only detects acoustic neuroma but also highlights its location and boundaries and estimates the size of neuroma. Moreover, the computational time associated with detection and size prediction is much less in comparison to the other existing techniques. Exceptional results were obtained with an over-All accuracy of 94.12% in the detection of Acoustic Neuroma during final experimentation.

AB - Acoustic neuroma is a benign brain tumour, if not identified and diagnosed at early stages can result in loss of hearing. In this study Multilayer Perceptron (MLP) a feedforward Artificial Neural Network model (ANN), has been investigated for segmentation and detection of acoustic neuroma at early stages using medical resonance imaging (MRI). The proposed methodology comprises of two phases. In first phase, regions in the MRI images were classified and segmented as affected (neuroma) or normal areas. The classification was performed on pixel level using MLP. During this phase Region of Interest (ROI) was created through domain-specific knowledge. The MLP was then trained using 1490 random samples. In the second phase acoustic neuroma was detected in MRI images. The statistical analyses were subsequently performed on the pixel level to calculate the current size of detected neuroma and to provide suggestive deadlines for treatment using the growth rate of the tumour. The methodology presented in this research is more efficient compared to state-of-The art practices. The proposed technique not only detects acoustic neuroma but also highlights its location and boundaries and estimates the size of neuroma. Moreover, the computational time associated with detection and size prediction is much less in comparison to the other existing techniques. Exceptional results were obtained with an over-All accuracy of 94.12% in the detection of Acoustic Neuroma during final experimentation.

KW - Acoustic Neuroma

KW - Image Processing

KW - Machine Vision

KW - Magnetic Resonance Image (MRI)

KW - Multi-Layer Perceptron

KW - Segmentation

U2 - 10.1166/jmihi.2017.2057

DO - 10.1166/jmihi.2017.2057

M3 - Review Article

VL - 7

SP - 371

EP - 377

JO - JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS

JF - JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS

SN - 2156-7018

IS - 2

ER -