###### Abstract  
Generally, traumatic and aneurysmal brain injuries cause intracranial hemorrhages, which is a severe disease that results in death, if it is not treated and diagnosed properly at the early stage. Compared to other imaging techniques, Computed Tomography (CT) images are extensively utilized by clinicians for locating and identifying intracranial hemorrhage regions. However, it is a time-consuming and complex task, which majorly depends on professional clinicians. To highlight this problem, a novel model is developed for the automatic detection of intracranial hemorrhages. After collecting the 3D CT scans from the Radiological Society of North America (RSNA) 2019 brain CT hemorrhage database, the image segmentation is carried out using Fuzzy C Means (FCM) clustering algorithm. Then, the hybrid feature extraction is accomplished on the segmented regions utilizing the Histogram of Oriented Gradients (HoG), Local Ternary Pattern (LTP), and Local Binary Pattern (LBP) to extract discriminative features. Furthermore, the Cuckoo Search Optimization (CSO) algorithm and the Optimized Gated Recurrent Unit (OGRU) classifier are integrated for feature selection and sub-type classification of intracranial hemorrhages. In the resulting segment, the proposed ORGU-CSO model obtained 99.36% of classification accuracy, which is higher related to other considered classifiers.  
CT images [9]. However, automated models play an essential role in the early detection of intracranial hemorrhage lesions. Where the computer-aided system offers consistent and quick results with improved classification accuracy that enables physicians in making effective strategic decisions and treatment planning. Therefore, an automated model is developed in this paper for effective intracranial hemorrhage lesions detection. The contributions of this work are listed as follows:  
* Developed FCM clustering algorithm for segmenting the diseased portions from the collected 3D brain scans. The FCM clustering algorithm gives good results in the overlapped database, and it is comparatively better than other considered clustering algorithms.
* Performed hybrid feature extraction using HoG, LTP, and LBP descriptors. The hybrid feature extraction includes advantages such as improved data visualization, a sped-up training process, an increase in explainability, and overfitting risk reduction.
* Developed CSO algorithm for feature optimization to diminish the dimension of the extracted feature vectors that reduce the complexity of the system and computational time.
* Proposed OGRU model for classifying 3D brain image classes, namely Intraparenchymal, Subdural, Subarachnoid, Intraventricular, Epidural, and any other. The proposed OGRU model includes Broyden Fletcher Goldfarb Shanno's (BFGS) algorithm to resolve the un-constrained non-linear optimization issues.
* Proposed OGRU-CSO model's efficiency is investigated by utilizing evaluation measures such as Matthews Correlation Coefficient (MCC), precision, f-measure, specificity, recall, and accuracy.  
This paper is organized as follows: Some recent articles related to intracranial hemorrhage detection are reviewed in Section 2. The mathematical derivations and the experimental evaluations of the OGRU-CSO model are represented in Sections 3 and 4. Lastly, the conclusion is denoted in Section 5.