An Efficient Detection of Intracranial Hematoma Using Window-Based Stacking and YOLOv5 Framework

V. Vidhya; Anjan Gudigar; U. Raghavendra; Sudipta Basak; Sankalp Mallappa; Ajay Hegde; Girish Menon

doi:10.1109/ICAECA56562.2023.10200714

An Efficient Detection of Intracranial Hematoma Using Window-Based Stacking and YOLOv5 Framework

V. Vidhya^*
, Anjan Gudigar
, U. Raghavendra
, Sudipta Basak
, Sankalp Mallappa
, Ajay Hegde
, Girish Menon

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Traumatic brain injury (TBI) is a burgeoning medical disorder across the world particularly among young adults and children. TBI can cause intracranial hematoma (ICH), a lethal condition which requires prompt and accurate interpretation of computed tomography (CT) images for timely diagnosis and treatment. Since manual detection of CT images is a tedious and operator-dependent task, a deep learning framework is proposed for locating and categorizing ICH for improved diagnostic performance. Firstly, the input images are processed by using various techniques such as local directional pattern, local binary pattern, and windowing. Then the single stage YOLOv5 object detection model with faster spatial pyramid pooling is applied to detect hematoma regions of various types in the brain. The proposed model using windowing approach realized an overall mAP of 0.969, precision of 0.945 and recall of 0.943. The experimental results from the research study demonstrated that the suggested framework can help radiologists in strategic decision-making and improve quality care to the patients.

Original language	English
Title of host publication	2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350306811
DOIs	https://doi.org/10.1109/ICAECA56562.2023.10200714
Publication status	Published - 2023
Event	2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2023 - Virtual, Online, India Duration: 16-06-2023 → 17-06-2023

Publication series

Name	2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2023

Conference

Conference	2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2023
Country/Territory	India
City	Virtual, Online
Period	16-06-23 → 17-06-23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

All Science Journal Classification (ASJC) codes

Computer Science Applications
Information Systems and Management
Energy Engineering and Power Technology
Renewable Energy, Sustainability and the Environment
Electrical and Electronic Engineering
Computer Networks and Communications
Control and Optimization

Access to Document

10.1109/ICAECA56562.2023.10200714

Cite this

Vidhya, V., Gudigar, A., Raghavendra, U., Basak, S., Mallappa, S., Hegde, A., & Menon, G. (2023). An Efficient Detection of Intracranial Hematoma Using Window-Based Stacking and YOLOv5 Framework. In 2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2023 (2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICAECA56562.2023.10200714

Vidhya, V. ; Gudigar, Anjan ; Raghavendra, U. et al. / An Efficient Detection of Intracranial Hematoma Using Window-Based Stacking and YOLOv5 Framework. 2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2023).

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title = "An Efficient Detection of Intracranial Hematoma Using Window-Based Stacking and YOLOv5 Framework",

abstract = "Traumatic brain injury (TBI) is a burgeoning medical disorder across the world particularly among young adults and children. TBI can cause intracranial hematoma (ICH), a lethal condition which requires prompt and accurate interpretation of computed tomography (CT) images for timely diagnosis and treatment. Since manual detection of CT images is a tedious and operator-dependent task, a deep learning framework is proposed for locating and categorizing ICH for improved diagnostic performance. Firstly, the input images are processed by using various techniques such as local directional pattern, local binary pattern, and windowing. Then the single stage YOLOv5 object detection model with faster spatial pyramid pooling is applied to detect hematoma regions of various types in the brain. The proposed model using windowing approach realized an overall mAP of 0.969, precision of 0.945 and recall of 0.943. The experimental results from the research study demonstrated that the suggested framework can help radiologists in strategic decision-making and improve quality care to the patients.",

author = "V. Vidhya and Anjan Gudigar and U. Raghavendra and Sudipta Basak and Sankalp Mallappa and Ajay Hegde and Girish Menon",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2023 ; Conference date: 16-06-2023 Through 17-06-2023",

year = "2023",

doi = "10.1109/ICAECA56562.2023.10200714",

language = "English",

series = "2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2023",

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}

Vidhya, V , Gudigar, A , Raghavendra, U, Basak, S, Mallappa, S, Hegde, A & Menon, G 2023, An Efficient Detection of Intracranial Hematoma Using Window-Based Stacking and YOLOv5 Framework. in 2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2023. 2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2023, Institute of Electrical and Electronics Engineers Inc., 2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2023, Virtual, Online, India, 16-06-23. https://doi.org/10.1109/ICAECA56562.2023.10200714

An Efficient Detection of Intracranial Hematoma Using Window-Based Stacking and YOLOv5 Framework. / Vidhya, V.; Gudigar, Anjan ; Raghavendra, U. et al.
2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - An Efficient Detection of Intracranial Hematoma Using Window-Based Stacking and YOLOv5 Framework

AU - Vidhya, V.

AU - Gudigar, Anjan

AU - Raghavendra, U.

AU - Basak, Sudipta

AU - Mallappa, Sankalp

AU - Hegde, Ajay

AU - Menon, Girish

PY - 2023

Y1 - 2023

N2 - Traumatic brain injury (TBI) is a burgeoning medical disorder across the world particularly among young adults and children. TBI can cause intracranial hematoma (ICH), a lethal condition which requires prompt and accurate interpretation of computed tomography (CT) images for timely diagnosis and treatment. Since manual detection of CT images is a tedious and operator-dependent task, a deep learning framework is proposed for locating and categorizing ICH for improved diagnostic performance. Firstly, the input images are processed by using various techniques such as local directional pattern, local binary pattern, and windowing. Then the single stage YOLOv5 object detection model with faster spatial pyramid pooling is applied to detect hematoma regions of various types in the brain. The proposed model using windowing approach realized an overall mAP of 0.969, precision of 0.945 and recall of 0.943. The experimental results from the research study demonstrated that the suggested framework can help radiologists in strategic decision-making and improve quality care to the patients.

AB - Traumatic brain injury (TBI) is a burgeoning medical disorder across the world particularly among young adults and children. TBI can cause intracranial hematoma (ICH), a lethal condition which requires prompt and accurate interpretation of computed tomography (CT) images for timely diagnosis and treatment. Since manual detection of CT images is a tedious and operator-dependent task, a deep learning framework is proposed for locating and categorizing ICH for improved diagnostic performance. Firstly, the input images are processed by using various techniques such as local directional pattern, local binary pattern, and windowing. Then the single stage YOLOv5 object detection model with faster spatial pyramid pooling is applied to detect hematoma regions of various types in the brain. The proposed model using windowing approach realized an overall mAP of 0.969, precision of 0.945 and recall of 0.943. The experimental results from the research study demonstrated that the suggested framework can help radiologists in strategic decision-making and improve quality care to the patients.

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UR - https://www.scopus.com/pages/publications/85168663579#tab=citedBy

U2 - 10.1109/ICAECA56562.2023.10200714

DO - 10.1109/ICAECA56562.2023.10200714

M3 - Conference contribution

AN - SCOPUS:85168663579

T3 - 2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2023

BT - 2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2023

Y2 - 16 June 2023 through 17 June 2023

ER -

Vidhya V , Gudigar A , Raghavendra U, Basak S, Mallappa S, Hegde A et al. An Efficient Detection of Intracranial Hematoma Using Window-Based Stacking and YOLOv5 Framework. In 2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2023). doi: 10.1109/ICAECA56562.2023.10200714

An Efficient Detection of Intracranial Hematoma Using Window-Based Stacking and YOLOv5 Framework

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