Fusion-Based Deep Learning with Nature-Inspired Algorithm for Intracerebral Haemorrhage Diagnosis

Abstract

Natural computing refers to computational processes observed in nature and human-designed computing inspired by nature. In recent times, data fusion in the healthcare sector becomes a challenging issue, and it needs to be resolved. At the same time, intracerebral haemorrhage (ICH) is the injury of blood vessels on the brain cells, which is mainly liable for stroke. X-rays and computed tomography (CT) scans are widely applied for locating the haemorrhage position and size. Since manual segmentation of the CT scans by planimetry by the use of radiologists is a time-consuming process, deep learning (DL) is used to attain effective ICH diagnosis performance. This paper presents an automated intracerebral haemorrhage diagnosis using fusion-based deep learning with swarm intelligence (AICH-FDLSI) algorithm. The AICH-FDLSI model operates on four major stages namely preprocessing, image segmentation, feature extraction, and classification. To begin with, the input image is preprocessed using the median filtering (MF) technique to remove the noise present in the image. Next, the seagull optimization algorithm (SOA) with Otsu multilevel thresholding is employed for image segmentation. In addition, the fusion-based feature extraction model using the Capsule Network (CapsNet) and EfficientNet is applied to extract a useful set of features. Moreover, deer hunting optimization (DHO) algorithm is utilized for the hyperparameter optimization of the CapsNet and DenseNet models. Finally, a fuzzy support vector machine (FSVM) is applied as a classification technique to identify the different classes of ICH. A set of simulations takes place to determine the diagnostic performance of the AICH-FDLSI model using the benchmark intracranial haemorrhage data set. The experimental outcome stated that the AICH-FDLSI model has reached a proficient performance over the compared methods in a significant way.

Detection and categorization of acute intracranial hemorrhage subtypes using a multilayer DenseNet‐ResNet architecture with improved random forest classifier

“…Tables 1–7 shows the simulation results obtained from the proposed acute ICH detection and classification model for training–testing % are illustrated below in a tabular form. Here, the proposed ICH‐DC‐ResNet‐DenseNet‐IRF method compared with existing deep learning algorithm for automatic detection with classification of acute intracranial hemorrhages in head CT scans (ICH‐DC‐2D‐CNN), 33 fusion‐based deep learning along nature‐inspired algorithm for the diagnosis of intracerebral hemorrhage (ICH‐DC‐FSVM) 34 and detection of intracranial hemorrhage on CT scan images utilizing convolutional neural network (ICH‐DC‐CNN), 35 ICH‐DC‐CNN‐FCNs 31 methods, respectively.…”

“…Then the sub types of ICH are intracerebral hemorrhage, intraventricular hemorrhage, epidural hemorrhage, subdural hemorrhage, subarachnoid hemorrhage is classified by using IRF classifier with high accuracy. automatic detection with classification of acute intracranial hemorrhages in head CT scans (ICH-DC-2D-CNN), 33 fusion-based deep learning along nature-inspired algorithm for the diagnosis of intracerebral hemorrhage (ICH-DC-FSVM) 34 and detection of intracranial hemorrhage on CT scan images utilizing convolutional neural network (ICH-DC-CNN), 35 ICH-DC-CNN-FCNs 31 methods, respectively. proposed ICH-DC-ResNet-DenseNet-IRF method provides 17.87%, 32.86%, 32.86%, and 34.97% higher recall than the existing methods.…”

Midpalatal Suture CBCT Image Quantitive Characteristics Analysis Based on Machine Learning Algorithm Construction and Optimization

“…Image fusion has been used in this study to extract multi-section image information and then synthesize high-quality fused images. While as a widely used medical image analysis method in studies of several diseases [ 33 , 34 , 35 ], image fusion has not been applied in craniofacial growth and development studies. The combination of image fusion and craniofacial growth analysis, especially skeletal growth analysis, can help us utilize comprehensive image information of the complicated structures effectively and reliably.…”

“…Image fusion is suitable for the comprehensive and high-quality extraction of image information from complex anatomical structures. It has been applied in craniocerebral hemorrhage and tumor, liver injury, etc., playing an important role in disease identification and diagnosis [ 33 , 34 , 35 ] and has started to be applied in oral and maxillofacial diseases, including head and neck tumor and temporal–mandibular joint diseases [ 36 , 37 ].…”

Research on Camouflaged Human Target Detection Based on Deep Learning

“…In the civilian field, target detection algorithms have played an important role in the fields of autonomous driving, intelligent navigation, medical image recognition, and search and rescue [ 4 , 5 ]. Especially when a natural disaster strikes unexpectedly, it may cause a large area of houses to collapse and cause casualties, so the top priority is to rescue the survivors in the rubble in time.…”