Nuclear atypia grading in breast cancer histopathological images based on CNN feature extraction and LSTM classification

Early diagnosis of breast cancer,the most common disease among women around the world,increases the chance of treatment and is highly important.Nuclear atypia grading in histopathological images plays an important role in the final diagnosis and grading ofbreast cancer.Grading images by pathologists is a time consuming and subjective task.Therefore,the existence of a computer-aided system for nuclear atypia grading is very useful and necessary;In this stud%two automatic systems for grading nuclear atypia in breast cancer histopathological images based on deep learning methods are proposed.A patch-based approach is introduced due to the large size of the histopathological images and restriction of the training data.In the proposed system I,the most important patches in the image are detected first and then a three-hidden-layer convolutional neural network(CNN)is designed and trained for feature extraction and to classify the patches individually.The proposed system II is based on a combination of the CNN for feature extraction and a two-layer Long short-term memoty(LSTM)network for classification.The LSTM network is utilised to consider all patches of an image simultaneously for image grading.The simulation results show the efficiency of the proposed systems for automatic nuclear atypia grading and outperform the current related studies in the literature.

Ensemble of Handcrafted and Deep Learning Model for Histopathological Image Classification

Histopathology is the investigation of tissues to identify the symptom of abnormality.The histopathological procedure comprises gathering samples of cells/tissues,setting them on the microscopic slides,and staining them.The investigation of the histopathological image is a problematic and laborious process that necessitates the expert’s knowledge.At the same time,deep learning(DL)techniques are able to derive features,extract data,and learn advanced abstract data representation.With this view,this paper presents an ensemble of handcrafted with deep learning enabled histopathological image classification(EHCDL-HIC)model.The proposed EHCDLHIC technique initially performs Weiner filtering based noise removal technique.Once the images get smoothened,an ensemble of deep features and local binary pattern(LBP)features are extracted.For the classification process,the bidirectional gated recurrent unit(BGRU)model can be employed.At the final stage,the bacterial foraging optimization(BFO)algorithm is utilized for optimal hyperparameter tuning process which leads to improved classification performance,shows the novelty of the work.For validating the enhanced execution of the proposed EHCDL-HIC method,a set of simulations is performed.The experimentation outcomes highlighted the betterment of the EHCDL-HIC approach over the existing techniques with maximum accuracy of 94.78%.Therefore,the EHCDL-HIC model can be applied as an effective approach for histopathological image classification.

LGFFN-GHI: A Local-Global Feature Fuse Network for Gastric Histopathological Image Classification

Gastric cancer remains the third most common cause of cancer-related death. Histopathological examination of gastric cancer is the gold standard for the diagnosis of gastric cancer. However, manual pathology examination is time-consuming and laborious. Computer-aided diagnosis (CAD) systems can assist pathologists in diagnosing pathological images, thus improving the efficiency of disease diagnosis. In this paper, we propose a two-branch network named LGFFN-GHI, which can classify histopathological images of gastric cancer into two categories: normal and abnormal. LGFFN-GHI consists of two parallel networks, ResNet18 and Pvt-Tiny, which extract local and global features of microscopic gastric tissue images, respectively. We propose a feature blending module (FFB) that fuses local and global features at the same resolution in a cross-attention manner. This enables ResNet18 to acquire the global features extracted by Pvt-Tiny, while enabling Pvt-Tiny to acquire the local features extracted by ResNet18. We conducted experiments on a novel publicly available subsize image database of gastric histopathology (GasHisSDB). The experimental results show that LGFFN-GHI achieves an accuracy of 96.814%, which is 2.388% and 3.918% better than the baseline methods ResNet18 and Pvt-Tiny, respectively. Our proposed network exhibits high classification performance, demonstrating its effectiveness and future potential for the gastric histopathology image classification (GHIC) task.

Integrating Transformer and Bidirectional Long Short-Term Memory for Intelligent Breast Cancer Detection from Histopathology Biopsy Images

Breast cancer is a significant threat to the global population,affecting not only women but also a threat to the entire population.With recent advancements in digital pathology,Eosin and hematoxylin images provide enhanced clarity in examiningmicroscopic features of breast tissues based on their staining properties.Early cancer detection facilitates the quickening of the therapeutic process,thereby increasing survival rates.The analysis made by medical professionals,especially pathologists,is time-consuming and challenging,and there arises a need for automated breast cancer detection systems.The upcoming artificial intelligence platforms,especially deep learning models,play an important role in image diagnosis and prediction.Initially,the histopathology biopsy images are taken from standard data sources.Further,the gathered images are given as input to the Multi-Scale Dilated Vision Transformer,where the essential features are acquired.Subsequently,the features are subjected to the Bidirectional Long Short-Term Memory(Bi-LSTM)for classifying the breast cancer disorder.The efficacy of the model is evaluated using divergent metrics.When compared with other methods,the proposed work reveals that it offers impressive results for detection.

Convolutional Neural Network for Histopathological Osteosarcoma Image Classification

Osteosarcoma is one of the most widespread causes of bone cancer globally and has a high mortality rate.Early diagnosis may increase the chances of treatment and survival however the process is time-consuming(reliability and complexity involved to extract the hand-crafted features)and largely depends on pathologists’experience.Convolutional Neural Network(CNN—an end-to-end model)is known to be an alternative to overcome the aforesaid problems.Therefore,this work proposes a compact CNN architecture that has been rigorously explored on a Small Osteosarcoma histology Image Dataaseet(a high-class imbalanced dataset).Though,during training,class-imbalanced data can negatively affect the performance of CNN.Therefore,an oversampling technique has been proposed to overcome the aforesaid issue and improve generalization performance.In this process,a hierarchical CNN model is designed,in which the former model is non-regularized(due to dense architecture)and the later one is regularized,specifically designed for small histopathology images.Moreover,the regularized model is integrated with CNN’s basic architecture to reduce overfitting.Experimental results demonstrate that oversampling might be an effective way to address the imbalanced class problem during training.The training and testing accuracies of the non-regularized CNN model are 98%&78%with an imbalanced dataset and 96%&81%with a balanced dataset,respectively.The regularized CNN model training and testing accuracies are 84%&75%for an imbalanced dataset and 87%&86%for a balanced dataset.

Nuclei Segmentation in Histopathology Images Using Structure-Preserving Color Normalization Based Ensemble Deep Learning Frameworks

This paper presents a novel computerized technique for the segmentation of nuclei in hematoxylin and eosin(H&E)stained histopathology images.The purpose of this study is to overcome the challenges faced in automated nuclei segmentation due to the diversity of nuclei structures that arise from differences in tissue types and staining protocols,as well as the segmentation of variable-sized and overlapping nuclei.To this extent,the approach proposed in this study uses an ensemble of the UNet architecture with various Convolutional Neural Networks(CNN)architectures as encoder backbones,along with stain normalization and test time augmentation,to improve segmentation accuracy.Additionally,this paper employs a Structure-Preserving Color Normalization(SPCN)technique as a preprocessing step for stain normalization.The proposed model was trained and tested on both single-organ and multi-organ datasets,yielding an F1 score of 84.11%,mean Intersection over Union(IoU)of 81.67%,dice score of 84.11%,accuracy of 92.58%and precision of 83.78%on the multi-organ dataset,and an F1 score of 87.04%,mean IoU of 86.66%,dice score of 87.04%,accuracy of 96.69%and precision of 87.57%on the single-organ dataset.These findings demonstrate that the proposed model ensemble coupled with the right pre-processing and post-processing techniques enhances nuclei segmentation capabilities.

A comparative study on machine learning-based classification to find photothrombotic lesion in histological rabbit brain images

Recently,research has been conducted to assist in the processing and analysis of histopathological images using machine learning algorithms.In this study,we established machine learning-based algorithms to detect photothrombotic lesions in histological images of photothrombosis-induced rabbit brains.Six machine learning-based algorithms for binary classification were applied,and the accu-racies were compared to classify normal tissues and photothrombotic lesions.The lesion classification model consisting of a 3-layered neural network with a rectified linear unit(ReLU)activation function,Xavier initialization,and Adam optimization using datasets with a unit size of 128×128 pixels yielded the highest accuracy(0.975).In the validation using the tested histological images,it was confirmed that the model could identify regions where brain damage occurred due to photochemical ischemic stroke.Through the development of machine learning-based photothrombotic lesion classi-fication models and performance comparisons,we confirmed that machine learning algorithms have the potential to be utilized in histopathology and various medical diagnostic techniques.

Optimal Deep Transfer Learning Model for Histopathological Breast Cancer Classification

Earlier recognition of breast cancer is crucial to decrease the severity and optimize the survival rate.One of the commonly utilized imaging modalities for breast cancer is histopathological images.Since manual inspection of histopathological images is a challenging task,automated tools using deep learning(DL)and artificial intelligence(AI)approaches need to be designed.The latest advances of DL models help in accomplishing maximum image classification performance in several application areas.In this view,this study develops a Deep Transfer Learning with Rider Optimization Algorithm for Histopathological Classification of Breast Cancer(DTLRO-HCBC)technique.The proposed DTLRO-HCBC technique aims to categorize the existence of breast cancer using histopathological images.To accomplish this,the DTLRO-HCBC technique undergoes pre-processing and data augmentation to increase quantitative analysis.Then,optimal SqueezeNet model is employed for feature extractor and the hyperparameter tuning process is carried out using the Adadelta optimizer.Finally,rider optimization with deep feed forward neural network(RO-DFFNN)technique was utilized employed for breast cancer classification.The RO algorithm is applied for optimally adjusting the weight and bias values of the DFFNN technique.For demonstrating the greater performance of the DTLRO-HCBC approach,a sequence of simulations were carried out and the outcomes reported its promising performance over the current state of art approaches.

ResNet50-Based Effective Model for Breast Cancer Classification Using Histopathology Images

Breast cancer is considered an immense threat and one of the leading causes of mortality in females.It is curable only when detected at an early stage.A standard cancer diagnosis approach involves detection of cancer-related anomalies in tumour histopathology images.Detection depends on the accurate identification of the landmarks in the visual artefacts present in the slide images.Researchers are continuously striving to develop automatic machine-learning algorithms for processing medical images to assist in tumour detection.Nowadays,computerbased automated systems play an important role in cancer image analysis and help healthcare experts make rapid and correct inferences about the type of cancer.This study proposes an effective convolutional neural networkbased(CNN-based)model that exploits the transfer-learning technique for automatic image classification between malignant and benign tumour,using histopathology images.Resnet50 architecture has been trained on new dataset for feature extraction,and fully connected layers have been fine-tuned for achieving highest training,validation and test accuracies.The result illustrated state-of-the-art performance of the proposed model with highest training,validation and test accuracies as 99.70%,99.24%and 99.24%,respectively.Classification accuracy is increased by 0.66%and 0.2%when compared with similar recent studies on training and test data results.Average precision and F1 score have also improved,and receiver operating characteristic(RoC)area has been achieved to 99.1%.Thus,a reliable,accurate and consistent CNN model based on pre-built Resnet50 architecture has been developed.

Deep Learning Framework for the Prediction of Childhood Medulloblastoma

This research work develops new and better prognostic markers for predicting Childhood MedulloBlastoma(CMB)using a well-defined deep learning architecture.A deep learning architecture could be designed using ideas from image processing and neural networks to predict CMB using histopathological images.First,a convolution process transforms the histopathological image into deep features that uniquely describe it using different two-dimensional filters of various sizes.A 10-layer deep learning architecture is designed to extract deep features.The introduction of pooling layers in the architecture reduces the feature dimension.The extracted and dimension-reduced deep features from the arrangement of convolution layers and pooling layers are used to classify histopathological images using a neural network classifier.The performance of the CMB classification system is evaluated using 1414(10×magnification)and 1071(100×magnification)augmented histopathological images with five classes of CMB such as desmoplastic,nodular,large cell,classic,and normal.Experimental results show that the average classification accuracy of 99.38%(10×)and 99.07%(100×)is attained by the proposed CNB classification system.

Hyperparameter Tuning Bidirectional Gated Recurrent Unit Model for Oral Cancer Classification

Oral Squamous Cell Carcinoma(OSCC)is a type of Head and Neck Squamous Cell Carcinoma(HNSCC)and it should be diagnosed at early stages to accomplish efficient treatment,increase the survival rate,and reduce death rate.Histopathological imaging is a wide-spread standard used for OSCC detection.However,it is a cumbersome process and demands expert’s knowledge.So,there is a need exists for automated detection ofOSCC using Artificial Intelligence(AI)and Computer Vision(CV)technologies.In this background,the current research article introduces Improved Slime Mould Algorithm with Artificial Intelligence Driven Oral Cancer Classification(ISMA-AIOCC)model on Histopathological images(HIs).The presented ISMA-AIOCC model is aimed at identification and categorization of oral cancer using HIs.At the initial stage,linear smoothing filter is applied to eradicate the noise from images.Besides,MobileNet model is employed to generate a useful set of feature vectors.Then,Bidirectional Gated Recurrent Unit(BGRU)model is exploited for classification process.At the end,ISMA algorithm is utilized to fine tune the parameters involved in BGRU model.Moreover,ISMA algorithm is created by integrating traditional SMA and ChaoticOppositional Based Learning(COBL).The proposed ISMA-AIOCC model was validated for performance using benchmark dataset and the results pointed out the supremacy of ISMA-AIOCC model over other recent approaches.

Application of transfer learning and ensemble learning in image-level classification for breast histopathology

Background Breast cancer has the highest prevalence among all cancers in women globally.The classification of histopathological images in the diagnosis of breast cancers is an area of clinical concern.In computer-aided diagnosis,most traditional classification models use a single network to extract features,although this approach has significant limitations.Moreover,many networks are trained and optimized on patient-level datasets,ignoring lower-level data labels.Methods This paper proposed a deep ensemble model based on image-level labels for the binary classification of breast histopathological images of benign and malignant lesions.First,the BreaKHis dataset was randomly divided into training,validation,and test sets.Then,data augmentation techniques were used to balance the numbers of benign and malignant samples.Third,based on their transfer learning performance and the complementarity between networks,VGG16,Xception,ResNet50,and DenseNet201 were selected as base classifiers.Results In a ensemble network model with accuracy as the weight,the image-level binary classification achieved an accuracy of 98.90%.To verify the capabilities of our method,it was experimentally compared with the latest transformer and multilayer perception(MLP)models on the same dataset.Our ensemble model showed a 5%-20%advantage,emphasizing its far-reaching abilities in classification tasks.Conclusions This research focuses on improving the performance of a classification model with an ensemble algorithm.Transfer learning has an essential role in classification of small datasets,improving training speed and accuracy.Our model may outperform many existing approaches with respect to accuracy and has applications in the field of auxiliary medical diagnosis.

Correlation of MR imaging and histopathology after partial resection of normal rabbit brain

Abstract Objectives To investigate the findings of magnetic resonance (MR) imaging and histopathology in early postoperative normal brain, and to define the correlation between MR images and histopathology. Methods Thirty-six New Zealand rabbits weighing 2.0 to 3.0*!kg were divided into 10 groups according to different postoperative days: 1 to 10 days. A partial resection of the parietooccipital region was performed under usual aseptic conditions after the animals were anesthetized intravenously with 3% pentobarbital (30*!mg/kg). MR imaging procedures consisted of pre- and postcontrast scanning and were carried out on postoperative days 1 to 10. Brain tissue samples were prepared for examination immediately after MR scanning. Histopathological examination was done under light both and electron microscopes. The findings of MR imaging were compared with histopathologic findings.Results Surgical margin contrast enhancement on MR images could be seen 24 hours after surgery. The degree of contrast enhancement increased gradually up to 5 days postoperation, and no remarkable changes were present from days 5 to 10. Disruption of the blood brain barrier (BBB) was the main cause of contrast enhancement during the first 3 postoperative days. After that period, the mechanism responsible for contrast enhancement was the formation of neovascularity and a broken BBB. An increase in the amount of neovascularity played a predominant role in contrast enhancement in normal postoperative brain tissue. Conclusions The features of enhanced MR images present at the surgical margin followed a typical time course during the early postoperative period. The role of neovascularity and BBB disruption in the formation of contrast enhancement at the surgical margin varies with time. Knowledge of the features of contrast enhancement in postoperative MR images of normal brain can help in differentiating benign changes from residual malignant glioma.