Diagnosis of cerebral microbleed via VGG and extreme learning machine trained by Gaussian map bat algorithm

Abstract

Cerebral microbleed (CMB) is a serious public health concern. It is associated with dementia, which can be detected with brain magnetic resonance image (MRI). CMBs often appear as tiny round dots on MRIs, and they can be spotted anywhere over brain. Therefore, manual inspection is tedious and lengthy, and the results are often short in reproducible. In this paper, a novel automatic CMB diagnosis method was proposed based on deep learning and optimization algorithms, which used the brain MRI as the input and output the diagnosis results as CMB and non-CMB. Firstly, sliding window processing was employed to generate the dataset from brain MRIs. Then, a pre-trained VGG was employed to obtain the image features from the dataset. Finally, an ELM was trained by Gaussian-map bat algorithm (GBA) for identification. Results showed that the proposed method VGG-ELM-GBA provided better generalization performance than several state-of-the-art approaches.

Convolutional Extreme Learning Machines: A Systematic Review

“…The competitiveness of the results is clear when, in many cases, CELM was superior to several traditional models such as MLP (as in [102], [69], [50]) e SVM (as in [46], [113], [90]). Observing these results, we reported a good generalization and good representativeness by CELM [104], [68], [97], [27], [55], [57], [49].…”

“…Dataset [64] Classification of digestive organs disease Own dataset [65] Liver tumor classification Elazig University Hospital [66] White blood cell detection BCCD dataset [67] Histopathological image classification ADL dataset [68] Cerebral microbleed diagnosis Own dataset [69] Cervical cancer classification Herlev dataset [61] Brain tumor classification CGA-GBM database [70] Micro-nodules classification LIDC/IDRI dataset [62] Brain tumor classification Brain T1-weighed CE-MRI dataset [63] Brain tumor classification Brain tumor MRI dataset [71] Classification of anomalies in the human retina Duke and HUCM datasets [72] Hepatocellular carcinoma classification ICPR 2014 HEp-2 cell dataset [73]. Several works proposed digit and character recognition for applications such as handwriting recognition [73].…”

“…We reported the use of VGG-16 [31], [68], [115], [69], [57], [66] and VGG-19 [100] architectures for extracting features and fine-tuning with ELM, all previously mentioned works used pre-trained weights from ILSVRC dataset. The work [83] presented an approach to predict and classify data using a multimodal approach, where video data (frame sequencing) and audio are considered.…”

Convolutional Extreme Learning Machines: A Systematic Review

“…Dataset [64] Classification of digestive organs disease Own dataset [65] Liver tumor classification Elazig University Hospital [66] White blood cell detection BCCD dataset [67] Histopathological image classification ADL dataset [68] Cerebral microbleed diagnosis Own dataset [69] Cervical cancer classification Herlev dataset [61] Brain tumor classification CGA-GBM database [70] Micro-nodules classification LIDC/IDRI dataset [62] Brain tumor classification Brain T1-weighed CE-MRI dataset [63] Brain tumor classification Brain tumor MRI dataset [71] Classification of anomalies in the human retina Duke and HUCM datasets [72] Hepatocellular carcinoma classification ICPR 2014 HEp-2 cell dataset…”

“…We reported the use of VGG-16 [31,57,66,68,69,115] and VGG-19 [100] architectures for extracting features and fine-tuning with ELM; all of the previously mentioned studies used pre-trained weights from the ILSVRC dataset. The work [83] presented an approach to predict and classify data using a multimodal approach, where video data (frame sequencing) and audio are considered.…”