Hyperparameter tuned deep learning-driven medical image analysis for intracranial hemorrhage detection

Abstract

Intracranial haemorrhage (ICH) is a crucial medical emergency that entails prompt assessment and management. Compared to conventional clinical tests, the need for computerized medical assistance for properly recognizing brain haemorrhage from computer tomography (CT) scans is more mandatory. Various deep learning (DL) and artificial intelligence (AI) technologies have been successfully implemented for the analysis of medical images, namely grading of diabetic retinopathy (DR), breast cancer detection, skin cancer detection, and so on. Furthermore, the AI approach ensures accurate detection to facilitate early detection, drastically decreasing the mortality rate. Based on DL models, there are already various techniques for ICHdetection. This manuscript proposes the design of a Hyperparameter Tuned Deep Learning-Driven Medical Image Analysis for Intracranial Hemorrhage Detection (HPDL-MIAIHD) technique. The proposed HPDL-MIAIHD technique investigates the available CT images to classify and identify the ICH. In the presented HPDL-MIAIHD technique, the median filtering (MF) approach is utilized for the image preprocessing step. Next, the HPDL-MIAIHD approach uses an enhanced EfficientNet technique to extract feature vectors. To increase the efficiency of the EfficientNet method, the hyperparameter tuning process is performed by utilizing the chimp optimizer algorithm (COA) method. The ICH detection process is accomplished by the ensemble classification process, comprising long short-term memory (LSTM), stacked autoencoder (SAE), and bidirectional LSTM (Bi-LSTM) networks. Lastly, the Bayesian optimizer algorithm (BOA) is implemented for the hyperparameter selection of the DL technique. A comprehensive simulation was conducted on the benchmark CT image dataset to demonstrate the effectiveness of the HPDL-MIAIHD approach in detecting ICH. The performance validation of the HPDL-MIAIHD approach portrayed a superior accuracy value of 99.02% over other existing models.