Real-Time Multi-Feature Approximation Model-Based Efficient Brain Tumor Classification Using Deep Learning Convolution Neural Network Model

The deep learning models are identified as having a significant impact on various problems.The same can be adapted to the problem of brain tumor classification.However,several deep learning models are presented earlier,but they need better classification accuracy.An efficient Multi-Feature Approximation Based Convolution Neural Network(CNN)model(MFACNN)is proposed to handle this issue.The method reads the input 3D Magnetic Resonance Imaging(MRI)images and applies Gabor filters at multiple levels.The noise-removed image has been equalized for its quality by using histogram equalization.Further,the features like white mass,grey mass,texture,and shape are extracted from the images.Extracted features are trained with deep learning Convolution Neural Network(CNN).The network has been designed with a single convolution layer towards dimensionality reduction.The texture features obtained from the brain image have been transformed into a multi-dimensional feature matrix,which has been transformed into a single-dimensional feature vector at the convolution layer.The neurons of the intermediate layer are designed to measure White Mass Texture Support(WMTS),GrayMass Texture Support(GMTS),WhiteMass Covariance Support(WMCS),GrayMass Covariance Support(GMCS),and Class Texture Adhesive Support(CTAS).In the test phase,the neurons at the intermediate layer compute the support as mentioned above values towards various classes of images.Based on that,the method adds a Multi-Variate Feature Similarity Measure(MVFSM).Based on the importance ofMVFSM,the process finds the class of brain image given and produces an efficient result.

Improving Brain Tumor Classification with Deep Learning Using Synthetic Data

Deep learning(DL)techniques,which do not need complex preprocessing and feature analysis,are used in many areas of medicine and achieve promising results.On the other hand,in medical studies,a limited dataset decreases the abstraction ability of the DL model.In this context,we aimed to produce synthetic brain images including three tumor types(glioma,meningioma,and pituitary),unlike traditional data augmentation methods,and classify them with DL.This study proposes a tumor classification model consisting of a Dense Convolutional Network(DenseNet121)-based DL model to prevent forgetting problems in deep networks and delay information flow between layers.By comparing models trained on two different datasets,we demonstrated the effect of synthetic images generated by Cycle Generative Adversarial Network(CycleGAN)on the generalization of DL.One model is trained only on the original dataset,while the other is trained on the combined dataset of synthetic and original images.Synthetic data generated by CycleGAN improved the best accuracy values for glioma,meningioma,and pituitary tumor classes from 0.9633,0.9569,and 0.9904 to 0.9968,0.9920,and 0.9952,respectively.The developed model using synthetic data obtained a higher accuracy value than the related studies in the literature.Additionally,except for pixel-level and affine transform data augmentation,synthetic data has been generated in the figshare brain dataset for the first time.

Brain Cancer Tumor Classification from Motion-Corrected MRI Images Using Convolutional Neural Network

Detection of brain tumors in MRI images is the first step in brain cancer diagnosis.The accuracy of the diagnosis depends highly on the expertise of radiologists.Therefore,automated diagnosis of brain cancer from MRI is receiving a large amount of attention.Also,MRI tumor detection is usually followed by a biopsy(an invasive procedure),which is a medical procedure for brain tumor classification.It is of high importance to devise automated methods to aid radiologists in brain cancer tumor diagnosis without resorting to invasive procedures.Convolutional neural network(CNN)is deemed to be one of the best machine learning algorithms to achieve high-accuracy results in tumor identification and classification.In this paper,a CNN-based technique for brain tumor classification has been developed.The proposed CNN can distinguish between normal(no-cancer),astrocytoma tumors,gliomatosis cerebri tumors,and glioblastoma tumors.The implemented CNN was tested on MRI images that underwent a motion-correction procedure.The CNN was evaluated using two performance measurement procedures.The first one is a k-fold cross-validation testing method,in which we tested the dataset using k=8,10,12,and 14.The best accuracy for this procedure was 96.26%when k=10.To overcome the over-fitting problem that could be occurred in the k-fold testing method,we used a hold-out testing method as a second evaluation procedure.The results of this procedure succeeded in attaining 97.8%accuracy,with a specificity of 99.2%and a sensitivity of 97.32%.With this high accuracy,the developed CNN architecture could be considered an effective automated diagnosis method for the classification of brain tumors from MRI images.

Cartesian Product Based Transfer Learning Implementation for Brain Tumor Classification

Knowledge-based transfer learning techniques have shown good performance for brain tumor classification,especially with small datasets.However,to obtain an optimized model for targeted brain tumor classification,it is challenging to select a pre-trained deep learning(DL)model,optimal values of hyperparameters,and optimization algorithm(solver).This paper first presents a brief review of recent literature related to brain tumor classification.Secondly,a robust framework for implementing the transfer learning technique is proposed.In the proposed framework,a Cartesian product matrix is generated to determine the optimal values of the two important hyperparameters:batch size and learning rate.An extensive exercise consisting of 435 simulations for 11 state-of-the-art pre-trained DL models was performed using 16 paired hyperparameters from the Cartesian product matrix to input the model with the three most popular solvers(stochastic gradient descent with momentum(SGDM),adaptive moment estimation(ADAM),and root mean squared propagation(RMSProp)).The 16 pairs were formed using individual hyperparameter values taken from literature,which generally addressed only one hyperparameter for optimization,rather than making a grid for a particular range.The proposed framework was assessed using a multi-class publicly available dataset consisting of glioma,meningioma,and pituitary tumors.Performance assessment shows that ResNet18 outperforms all other models in terms of accuracy,precision,specificity,and recall(sensitivity).The results are also compared with existing state-of-the-art research work that used the same dataset.The comparison was mainly based on performance metric“accuracy”with support of three other parameters“precision,”“recall,”and“specificity.”The comparison shows that the transfer learning technique,implemented through our proposed framework for brain tumor classification,outperformed all existing approaches.To the best of our knowledge,the proposed framework is an efficient framework that helped reduce the computational complexity and the time to attain optimal values of two important hyperparameters and consequently the optimized model with an accuracy of 99.56%.

A Novel Approach to Breast Tumor Detection: Enhanced Speckle Reduction and Hybrid Classification in Ultrasound Imaging

Breast cancer detection heavily relies on medical imaging, particularly ultrasound, for early diagnosis and effectivetreatment. This research addresses the challenges associated with computer-aided diagnosis (CAD) of breastcancer fromultrasound images. The primary challenge is accurately distinguishing between malignant and benigntumors, complicated by factors such as speckle noise, variable image quality, and the need for precise segmentationand classification. The main objective of the research paper is to develop an advanced methodology for breastultrasound image classification, focusing on speckle noise reduction, precise segmentation, feature extraction, andmachine learning-based classification. A unique approach is introduced that combines Enhanced Speckle ReducedAnisotropic Diffusion (SRAD) filters for speckle noise reduction, U-NET-based segmentation, Genetic Algorithm(GA)-based feature selection, and Random Forest and Bagging Tree classifiers, resulting in a novel and efficientmodel. To test and validate the hybrid model, rigorous experimentations were performed and results state thatthe proposed hybrid model achieved accuracy rate of 99.9%, outperforming other existing techniques, and alsosignificantly reducing computational time. This enhanced accuracy, along with improved sensitivity and specificity,makes the proposed hybrid model a valuable addition to CAD systems in breast cancer diagnosis, ultimatelyenhancing diagnostic accuracy in clinical applications.

Overview of the 2022 WHO Classification of Pituitary Adenomas/Pituitary Neuroendocrine Tumors:Clinical Practices,Controversies,and Perspectives

The latest edition of the WHO classification of the central nervous system was published in 2021.This review summarizes the major revisions to the classification of anterior pituitary tumors.The most important revision involves preferring the terminology of pituitary neuroendocrine tumor(PitNET),even though the terminology of pituitary adenoma(PA)still can be used according to this WHO classification compared to the previous one.Moreover,immunohistochemistry(IHC)examination of pituitary-specific transcription factors(TFs),including PIT1,TPIT,SF-1,GATA2/3,and ERα,is endorsed to determine the tumor cell lineage and to facilitate the classification of PitNET/PA subgroups.However,TF-negative IHC staining indicates PitNET/PA with no distinct cell lineages,which includes unclassified plurihormonal(PH)tumors and null cell(NC)tumors in this edition.The new WHO classification of PitNET/PA has incorporated tremendous advances in the understanding of the cytogenesis and pathogenesis of pituitary tumors.However,due to the shortcomings of the technology used in the diagnosis of PitNET/PA and the limited understanding of the tumorigenesis of PitNET/PA,the application of this new classification system in practice should be further evaluated and validated.Besides providing information for deciding the follow-up plans and adjunctive treatment after surgery,this classification system offers no additional help for neurosurgeons in clinical practice,especially in determining the treatment strategies.Therefore,it is necessary for neurosurgeons to establish a comprehensive pituitary classification system for PitNET/PA that incorporates neuroimaging grading data or direct observation of invasiveness during operation or the predictor of prognosis,as well as pathological diagnosis,thereby distinguishing the invasiveness of the tumor and facilitating neurosurgeons to decide on the treatment strategies and follow-up plans as well as adjunctive treatment after surgery.

Layer-Wise Pre-Training Low-Rank NMF Model for Mammogram-Based Breast Tumor Classification

Image-based breast tumor classification is an active and challenging problem.In this paper,a robust breast tumor classification framework is presented based on deep feature representation learning and exploiting available information in existing samples.Feature representation learning of mammograms is fulfilled by a modified nonnegative matrix factorization model called LPML-LRNMF,which is motivated by hierarchical learning and layer-wise pre-training(LP)strategy in deep learning.Low-rank(LR)constraint is integrated into the feature representation learning model by considering the intrinsic characteristics of mammograms.Moreover,the proposed LPML-LRNMF model is optimized via alternating direction method of multipliers and the corresponding convergence is analyzed.For completing classification,an inverse projection sparse representation model is introduced to exploit information embedded in existing samples,especially in test ones.Experiments on the public dataset and actual clinical dataset show that the classification accuracy,specificity and sensitivity achieve the clinical acceptance level.

The classification and microsurgery of magnum foramen tumor

Objective To explore classification and surgical approach of magnum foramen tumor. Methods A retrospective analysis was performed for 43 surgically treated patients with tumors involving foramen magnum. According to the site of tumor,the classification was divided into:Type Ⅰ,located at dorsal,Ⅰ a extra-medullary,

Efficient Computer Aided Diagnosis System for Hepatic Tumors Using Computed Tomography Scans

One of the leading causes of mortality worldwide is liver cancer.The earlier the detection of hepatic tumors,the lower the mortality rate.This paper introduces a computer-aided diagnosis system to extract hepatic tumors from computed tomography scans and classify them into malignant or benign tumors.Segmenting hepatic tumors from computed tomography scans is considered a challenging task due to the fuzziness in the liver pixel range,intensity values overlap between the liver and neighboring organs,high noise from computed tomography scanner,and large variance in tumors shapes.The proposed method consists of three main stages;liver segmentation using Fast Generalized Fuzzy C-Means,tumor segmentation using dynamic thresholding,and the tumor’s classification into malignant/benign using support vector machines classifier.The performance of the proposed system was evaluated using three liver benchmark datasets,which are MICCAI-Sliver07,LiTS17,and 3Dircadb.The proposed computer adided diagnosis system achieved an average accuracy of 96.75%,sensetivity of 96.38%,specificity of 95.20%and Dice similarity coefficient of 95.13%.

Identification of lncRNAs associated with T cells as potentialbiomarkers and therapeutic targets in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the most common and deadliest subtype of lung cancer. To select moretargeted and effective treatments for individuals, further advances in classifying LUAD are urgently needed. Thenumber, type, and function of T cells in the tumor microenvironment (TME) determine the progression andtreatment response of LUAD. Long noncoding RNAs (lncRNAs), may regulate T cell differentiation, development,and activation. Thus, our aim was to identify T cell-related lncRNAs (T cell-Lncs) in LUAD and to investigatewhether T cell-Lncs could serve as potential stratifiers and therapeutic targets. Seven T cell-Lncs were identified tofurther establish the T cell-related lncRNA risk score (TRS) in LUAD. Low TRS individuals were characterized byrobust immune status, fewer genomic alterations, and remarkably longer survival than high TRS individuals. Theexcellent accuracy of TRS in predicting overall survival (OS) was validated in the TCGA-LUAD training cohort andthe GEO-LUAD validation cohort. Our data demonstrated the favorable predictive power of the TRS-basednomogram, which had important clinical significance in estimating the survival probability for individuals. Inaddition, individuals with low TRS could respond better to chemotherapy and immunotherapy than those with highTRS. LINC00525 was identified as a valuable study target, and the ability of LUAD to proliferate or invade wassignificantly attenuated by downregulation of LINC00525. In conclusion, the TRS established by T cell-Lncs couldunambiguously classify LUAD patients, predict their prognosis and guide their management. Moreover, our identifiedT cell-Lncs could provide potential therapeutic targets for LUAD.

Multitask Learning with Multiscale Residual Attention for Brain Tumor Segmentation and Classification

Automatic segmentation and classification of brain tumors are of great importance to clinical treatment.However,they are challenging due to the varied and small morphology of the tumors.In this paper,we propose a multitask multiscale residual attention network(MMRAN)to simultaneously solve the problem of accurately segmenting and classifying brain tumors.The proposed MMRAN is based on U-Net,and a parallel branch is added at the end of the encoder as the classification network.First,we propose a novel multiscale residual attention module(MRAM)that can aggregate contextual features and combine channel attention and spatial attention better and add it to the shared parameter layer of MMRAN.Second,we propose a method of dynamic weight training that can improve model performance while minimizing the need for multiple experiments to determine the optimal weights for each task.Finally,prior knowledge of brain tumors is added to the postprocessing of segmented images to further improve the segmentation accuracy.We evaluated MMRAN on a brain tumor data set containing meningioma,glioma,and pituitary tumors.In terms of segmentation performance,our method achieves Dice,Hausdorff distance(HD),mean intersection over union(MIoU),and mean pixel accuracy(MPA)values of 80.03%,6.649 mm,84.38%,and 89.41%,respectively.In terms of classification performance,our method achieves accuracy,recall,precision,and F1-score of 89.87%,90.44%,88.56%,and 89.49%,respectively.Compared with other networks,MMRAN performs better in segmentation and classification,which significantly aids medical professionals in brain tumor management.The code and data set are available at https://github.com/linkenfaqiu/MMRAN.

Molecular pathology and clinical implications of diffuse glioma

The prognosis for diffusely infiltrating gliomas at World Health Organization(WHO)grade 2-4 remains dismal due to their heterogeneity.The rapid development of genome-wide molecular-profiling-associated studies has greatly promoted the accuracy of glioma classification.Thus,the latest version of the WHO classification of the central nervous system tumors published in 2021 has incorporated more molecular biomarkers together with histological features for the diagnosis of gliomas.Advanced usage of molecular pathology in clinical diagnostic practice provides also new opportunities for the therapy of patients with glioma,including surgery,radiotherapy and chemotherapy,targeted therapy,immunotherapy,and more precision clinical trials.Herein,we highlight the updates in the classification of gliomas according to the latest WHO guidelines and summarize the clinically relevant molecular markers by focusing on their applications in clinical practice.We also review the advances in molecular features of gliomas,which can facilitate the development of glioma therapies,thereby discussing the challenges and future directions of molecular pathology toward precision medicine for patients with glioma.