Meningioma brain tumor detection and classification using hybrid CNN method and RIDGELET transform

Brain tumor segmentation and classification using MRI: Modified segnet model and hybrid deep learning architecture with improved texture features

Brain tumors are quickly overtaking all other causes of death worldwide. The failure to perform a timely diagnosis is the main cause of increasing the death rate. Traditional methods of brain tumor diagnosis heavily rely on the expertise of radiologists, making timely and accurate diagnosis challenging. Magnetic Resonance Imaging (MRI) has emerged as the primary modality for brain tumor detection, but manual interpretation of MRI scans is time-consuming and error-prone. To address these challenges, an automated approach for brain tumor segmentation and classification (BTS&C) using MRI scans is proposed in this work. This work suggests a brain tumor classification scheme using MRI. Initially, the input images T1, TIC, t2 and t2 flair are fused via an improved fusion method. Then, Median Filtering (MF) is applied to preprocess the fused image. Also, the Modified Segnet model is proposed with a new pooling operation to do the segmentation process. Features like Improved local Gabor Binay pattern Histogram Sequence (ILGBPHS), Weber Local descriptor (WLD), and Tetrolet waveform are extracted from the segmented image. Finally, classification is done with HDLA that combines Bi-LSTM and Modified Linknet models. When TD= 90 %, the proposed method achieves a higher accuracy of 98 % which is compared to other methods like Bi-LSTM, Link Net, LeNet, Squeeze Net, Efficient Net, HHOCNN and CNN-SVM.

Classifications of meningioma brain images using the novel Convolutional Fuzzy C Means (CFCM) architecture and performance analysis of hardware incorporated tumor segmentation module

In this paper, meningioma detection and segmentation method is proposed. This research work proposes an effective method to locate meningioma pictures through a novel CFCM classification approach. This proposed method consist of Non-Sub sampled Contourlet Transform decomposition module which decomposes the entire brain image into multi-scale sub-band images and then the heuristic and uniqueness features have been computed individually. Then, these heuristic and uniqueness features are trained and classified using Convolutional Fuzzy C Means (CFCM) classifier. This proposed method is applied on two independent brain imaging datasets. The proposed meningioma identification system stated in this work obtained 98.81% of Se, 98.83% of Sp, 99.04% of Acc, 99.12% of pr, and 99.14% of FIS on Nanfang University dataset brain images. The proposed meningioma identification system stated in this work obtained 98.92% of Se, 98.88% of Sp, 98.9% of Acc, 98.88% of pr, and 99.36% of FIS on the BRATS 2021 brain images. Finally, the tumour segmentation module is designed in VLSI, and it is simulated using Xilinx project navigator in this paper.

Performance analysis of image retrieval system using deep learning techniques

The image retrieval is the process of retrieving the relevant images to the query image with minimal searching time in internet. The problem of the conventional Content-Based Image Retrieval (CBIR) system is that they produce retrieval results for either colour images or grey scale images alone. Moreover, the CBIR system is more complex which consumes more time period for producing the significant retrieval results. These problems are overcome through the proposed methodologies stated in this work. In this paper, the General Image (GI) and Medical Image (MI) are retrieved using deep learning architecture. The proposed system is designed with feature computation module, Retrieval Convolutional Neural Network (RETCNN) module, and Distance computation algorithm. The distance computation algorithm is used to compute the distances between the query image and the images in the datasets and produces the retrieval results. The average precision and recall for the proposed RETCNN-based CBIRS is 98.98% and 99.15% respectively for GI category, and the average precision and recall for the proposed RETCNN-based CBIRS are 99.04% and 98.89% respectively for MI category. The significance of these experimental results is used to produce the higher image retrieval rate of the proposed system.

Susceptibility-Weighted MRI for Predicting NF-2 Mutations and S100 Protein Expression in Meningiomas

S100 protein expression levels and neurofibromatosis type 2 (NF-2) mutations result in different disease courses in meningiomas. This study aimed to investigate non-invasive biomarkers of NF-2 copy number loss and S100 protein expression in meningiomas using morphological, radiomics, and deep learning-based features of susceptibility-weighted MRI (SWI). This retrospective study included 99 patients with S100 protein expression data and 92 patients with NF-2 copy number loss information. Preoperative cranial MRI was conducted using a 3T clinical MR scanner. Tumor volumes were segmented on fluid-attenuated inversion recovery (FLAIR) and subsequent registration of FLAIR to high-resolution SWI was performed. First-order textural features of SWI were extracted and assessed using Pyradiomics. Morphological features, including the tumor growth pattern, peritumoral edema, sinus invasion, hyperostosis, bone destruction, and intratumoral calcification, were semi-quantitatively assessed. Mann-Whitney U tests were utilized to assess the differences in the SWI features of meningiomas with and without S100 protein expression or NF-2 copy number loss. A logistic regression analysis was used to examine the relationship between these features and the respective subgroups. Additionally, a convolutional neural network (CNN) was used to extract hierarchical features of SWI, which were subsequently employed in a light gradient boosting machine classifier to predict the NF-2 copy number loss and S100 protein expression. NF-2 copy number loss was associated with a higher risk of developing high-grade tumors. Additionally, elevated signal intensity and a decrease in entropy within the tumoral region on SWI were observed in meningiomas with S100 protein expression. On the other hand, NF-2 copy number loss was associated with lower SWI signal intensity, a growth pattern described as "en plaque", and the presence of calcification within the tumor. The logistic regression model achieved an accuracy of 0.59 for predicting NF-2 copy number loss and an accuracy of 0.70 for identifying S100 protein expression. Deep learning features demonstrated a strong predictive capability for S100 protein expression (AUC = 0.85 ± 0.06) and had reasonable success in identifying NF-2 copy number loss (AUC = 0.74 ± 0.05). In conclusion, SWI showed promise in identifying NF-2 copy number loss and S100 protein expression by revealing neovascularization and microcalcification characteristics in meningiomas.

An automated cervical cancer diagnosis using genetic algorithm and CANFIS approaches

BACKGROUND

Cervical malignancy is considered among the most perilous cancers affecting women in numerous East African and South Asian nations, both in terms of its prevalence and fatality rates.

OBJECTIVE

This research aims to propose an efficient automated system for the segmentation of cancerous regions in cervical images.

METHODS

The proposed techniques encompass preprocessing, feature extraction with an optimized feature set, classification, and segmentation. The original cervical image undergoes smoothing using the Gaussian Filter technique, followed by the extraction of Local Binary Pattern (LBP) and Grey Level Co-occurrence Matrix (GLCM) features from the enhanced cervical images. LBP features capture pixel relationships within a mask window, while GLCM features quantify energy metrics across all pixels in the images. These features serve to distinguish normal cervical images from abnormal ones. The extracted features are optimized using Genetic Algorithm (GA) as an optimization method, and the optimized sets of features are classified using the Co-Active Adaptive Neuro-Fuzzy Inference System (CANFIS) classification method. Subsequently, a morphological segmentation technique is employed to categorize irregular cervical images, identifying and segmenting malignant regions within them.

RESULTS

The proposed approach achieved a sensitivity of 99.09%, specificity of 99.39%, and accuracy of 99.36%.

CONCLUSION

The proposed approach demonstrated superior performance compared to state-of-the-art techniques, and the results have been validated by expert radiologists.