Identification of Intracranial Hemorrhage using ResNeXt Model

K, Navaneet Bhat; Biradar, Vidyadevi G.; Mallya, Adithya Krishna S.; Sabat, Sohan Shekar; G, Mohan S.; Pareek, Piyush Kumar

doi:10.1109/mysurucon55714.2022.9972396

Cited by 8 publications

(1 citation statement)

References 14 publications

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“…The outcomes are measured using the standard performance measures described in Section 5.1 . The suggested model’s performance is compared to that of state-of-the-art techniques, including ResNexT [ 34 ], SVM, CNN [ 35 ], AlextNet + SVM [ 36 ], U-Net, and WA-ANN [ 37 ]. The Adam optimizer was used, employing a binary cross entropy loss and a learning proportion of 0.0001.…”

Section: Experimentation and Results Analysismentioning

confidence: 99%

An Efficient Framework to Detect Intracranial Hemorrhage Using Hybrid Deep Neural Networks

Rajagopal¹,

Buradagunta

Almeshari

et al. 2023

Brain Sciences

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Intracranial hemorrhage (ICH) is a serious medical condition that necessitates a prompt and exhaustive medical diagnosis. This paper presents a multi-label ICH classification issue with six different types of hemorrhages, namely epidural (EPD), intraparenchymal (ITP), intraventricular (ITV), subarachnoid (SBC), subdural (SBD), and Some. A patient may experience numerous hemorrhages at the same time in some situations. A CT scan of a patient’s skull is used to detect and classify the type of ICH hemorrhage(s) present. First, our model determines whether there is a hemorrhage or not; if there is a hemorrhage, the model attempts to identify the type of hemorrhage(s). In this paper, we present a hybrid deep learning approach that combines convolutional neural network (CNN) and Long-Short Term Memory (LSTM) approaches (Conv-LSTM). In addition, to propose viable solutions for the problem, we used a Systematic Windowing technique with a Conv-LSTM. To ensure the efficacy of the proposed model, experiments are conducted on the RSNA dataset. The suggested model provides higher sensitivity (93.87%), specificity (96.45%), precision (95.21%), and accuracy (95.14%). In addition, the obtained F1 score results outperform existing deep neural network-based algorithms.

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Section: Experimentation and Results Analysismentioning

confidence: 99%