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.

Revisiting the standards of cancer detection and therapy alongside their comparison to modern methods

In accordance with the World Health Organization data,cancer remains at the forefront of fatal diseases.An upward trend in cancer incidence and mortality has been observed globally,emphasizing that efforts in developing detection and treatment methods should continue.The diagnostic path typically begins with learning the medical history of a patient;this is followed by basic blood tests and imaging tests to indicate where cancer may be located to schedule a needle biopsy.Prompt initiation of diagnosis is crucial since delayed cancer detection entails higher costs of treatment and hospitalization.Thus,there is a need for novel cancer detection methods such as liquid biopsy,elastography,synthetic biosensors,fluorescence imaging,and reflectance confocal microscopy.Conventional therapeutic methods,although still common in clinical practice,pose many limitations and are unsatisfactory.Nowadays,there is a dynamic advancement of clinical research and the development of more precise and effective methods such as oncolytic virotherapy,exosome-based therapy,nanotechnology,dendritic cells,chimeric antigen receptors,immune checkpoint inhibitors,natural product-based therapy,tumor-treating fields,and photodynamic therapy.The present paper compares available data on conventional and modern methods of cancer detection and therapy to facilitate an understanding of this rapidly advancing field and its future directions.As evidenced,modern methods are not without drawbacks;there is still a need to develop new detection strategies and therapeutic approaches to improve sensitivity,specificity,safety,and efficacy.Nevertheless,an appropriate route has been taken,as confirmed by the approval of some modern methods by the Food and Drug Administration.

Breast Cancer Detection Based on Multi-Slotted Patch Antenna at ISM Band

The present work designed and investigated a 3D basic model for breast cancer detection at the ISM band. The model consists of two multi-slotted rectangular patch antennas and a three-layer breast phantom containing two tumors. A multi-slotted antenna was designed at 2.45 GHz using CST STUDIO SUITE 2018, where the simulated results showed a return loss better than -35 dB and attended more than 77 MHz bandwidth. The diagnosis approach is based on exploiting the electrical properties (frequency dependent) of breast tissues, i.e., mass density, relative permittivity, and conductivity. Once the proposed slotted antenna radiates electromagnetic signals toward the breast model (with and without tumors), the radiation properties in terms of the scattering parameters (S11 and S21), the electrical field, the power flow, the current density, and the power loss density were altered. As a result, the values of these radiation parameters increased when tumors were implanted inside the breast model, informing the presence of cancerous tissues. Moreover, the specific absorption rate (SAR) was estimated as a function of input powers, where the proposed antenna showed a set of low SAR values compared to the IEEE standard of 1.6 W/kg, validating its potential use for diagnosing purposes. The simulated results indicated the prospective use of two slotted antennas (in the first instance) to detect multiple tumors which could be a challenging task using a single-element antenna, where the ultimate goal is to realize a compact antenna array to detect multi-tumors.

Terahertz endoscopic imaging for colorectal cancer detection:Current status and future perspectives

Terahertz(THz) imaging is progressing as a robust platform for myriad applications in the field of security,health,and material science.The THz regime,which comprises wavelengths spanning from microns to millimeters,is non-ionizing and has very low photon energy:Making it inherently safe for biological imaging.Colorectal cancer is one of the most common causes of death in the world,while the conventional screening and standard of care yet relies exclusively on the physician's experience.Researchers have been working on the development of a flexible THz endoscope,as a potential tool to aid in colorectal cancer screening.This involves building a single-channel THz endoscope,and profiling the THz response from colorectal tissue,and demonstrating endogenous contrast levels between normal and diseased tissue when imaging in reflection modality.The current level of contrast provided by the prototype THz endoscopic system represents a significant step towards clinical endoscopic application of THz technology for invivo colorectal cancer screening.The aim of this paper is to provide a short review of the recent advances in THz endoscopic technology and cancer imaging.In particular,the potential of single-channel THz endoscopic imaging for colonic cancer screening will be highlighted.

Deep Learning-Based Cancer Detection-Recent Developments,Trend and Challenges

Cancer is one of the most critical diseases that has caused several deaths in today’s world.In most cases,doctors and practitioners are only able to diagnose cancer in its later stages.In the later stages,planning cancer treatment and increasing the patient’s survival rate becomes a very challenging task.Therefore,it becomes the need of the hour to detect cancer in the early stages for appropriate treatment and surgery planning.Analysis and interpretation of medical images such as MRI and CT scans help doctors and practitioners diagnose many diseases,including cancer disease.However,manual interpretation of medical images is costly,time-consuming and biased.Nowadays,deep learning,a subset of artificial intelligence,is gaining increasing attention from practitioners in automatically analysing and interpreting medical images without their intervention.Deep learning methods have reported extraordinary results in different fields due to their ability to automatically extract intrinsic features from images without any dependence on manually extracted features.This study provides a comprehensive review of deep learning methods in cancer detection and diagnosis,mainly focusing on breast cancer,brain cancer,skin cancer,and prostate cancer.This study describes various deep learningmodels and steps for applying deep learningmodels in detecting cancer.Recent developments in cancer detection based on deep learning methods have been critically analysed and summarised to identify critical challenges in applying them for detecting cancer accurately in the early stages.Based on the identified challenges,we provide a few promising future research directions for fellow researchers in the field.The outcome of this study provides many clues for developing practical and accurate cancer detection systems for its early diagnosis and treatment planning.

A double constrained robust capon beamforming based imaging method for early breast cancer detection

Ultra-wideband （UWB） microwave images are proposed for detecting small malignant breast tumors based on the large contrast of electric parameters between a malignant tumor and normal breast tissue. In this study, an antenna array composed of 9 antennas is applied to the detection. The double constrained robust capon beamforming （DCRCB） algorithm is used for reconstructing the breast image due to its better stability and high signal-to-interference-plus-noise ratio （SINR）. The successful detection of a tumor of 2 mm in diameter shown in the reconstruction demonstrates the robustness of the DCRCB beamforming algorithm. This study verifies the feasibility of detecting small breast tumors by using the DCRCB imaging algorithm.

A progressive processing method for breast cancer detection via UWB based on an MRI-derived model

Ultra-wideband （UWB） microwave imaging is a promising method for breast cancer detection based on the large contrast of electric parameters between the malignant tumor and its surrounded normal breast organisms. In the case of multiple tumors being present, the conventional imaging approaches may be ineffective to detect all the tumors clearly. In this paper, a progressive processing method is proposed for detecting more than one tumor. The method is divided into three stages： primary detection, refocusing and image optimization. To test the feasibility of the approach, a numerical breast model is developed based on the realistic magnetic resonance image （MRI）. Two tumors are assumed embedded in different positions. Successful detection of a 3.6 mm-diameter tumor at a depth of 42 mm is achieved. The correct information of both tumors is shown in the reconstructed image, suggesting that the progressive processing method is promising for multi-tumor detection.

Gaussian Optimized Deep Learning-based Belief Classification Model for Breast Cancer Detection

With the rapid increase of new cases with an increased mortality rate,cancer is considered the second and most deadly disease globally.Breast cancer is the most widely affected cancer worldwide,with an increased death rate percentage.Due to radiologists’processing of mammogram images,many computer-aided diagnoses have been developed to detect breast cancer.Early detection of breast cancer will reduce the death rate worldwide.The early diagnosis of breast cancer using the developed computer-aided diagnosis(CAD)systems still needed to be enhanced by incorporating innovative deep learning technologies to improve the accuracy and sensitivity of the detection system with a reduced false positive rate.This paper proposed an efficient and optimized deep learning-based feature selection approach with this consideration.This model selects the relevant features from the mammogram images that can improve the accuracy of malignant detection and reduce the false alarm rate.Transfer learning is used in the extraction of features initially.Na ext,a convolution neural network,is used to extract the features.The two feature vectors are fused and optimized with enhanced Butterfly Optimization with Gaussian function(TL-CNN-EBOG)to select the final most relevant features.The optimized features are applied to the classifier called Deep belief network(DBN)to classify the benign and malignant images.The feature extraction and classification process used two datasets,breast,and MIAS.Compared to the existing methods,the optimized deep learning-based model secured 98.6%of improved accuracy on the breast dataset and 98.85%of improved accuracy on the MIAS dataset.

Deep Transfer Learning Driven Oral Cancer Detection and Classification Model

Oral cancer is the most commonly occurring‘head and neck cancers’across the globe.Most of the oral cancer cases are diagnosed at later stages due to absence of awareness among public.Since earlier identification of disease is essential for improved outcomes,Artificial Intelligence(AI)and Machine Learning(ML)models are used in this regard.In this background,the current study introduces Artificial Intelligence with Deep Transfer Learning driven Oral Cancer detection and Classification Model(AIDTLOCCM).The primary goal of the proposed AIDTL-OCCM model is to diagnose oral cancer using AI and image processing techniques.The proposed AIDTL-OCCM model involves fuzzy-based contrast enhancement approach to perform data pre-processing.Followed by,the densely-connected networks(DenseNet-169)model is employed to produce a useful set of deep features.Moreover,Chimp Optimization Algorithm(COA)with Autoencoder(AE)model is applied for oral cancer detection and classification.Furthermore,COA is employed to determine optimal parameters involved in AE model.A wide range of experimental analyses was conducted on benchmark datasets and the results were investigated under several aspects.The extensive experimental analysis outcomes established the enhanced performance of AIDTLOCCM model compared to other approaches with a maximum accuracy of 90.08%.

Design and Analysis of Antipodal Vivaldi Antennas for Breast Cancer Detection

This paper presents the design and analysis of antipodal Vivaldi antennas(AVAs)for breast cancer detection.In order to enhance the antenna gain,different techniques such as using the uniform and non-uniform corrugation,expanding the dielectric substrate and adding the parasitic patch are applied to original AVA.The design procedure of two developed AVA structures i.e.,AVA with non-uniform corrugation and AVA with parasitic patch are presented.The proposed AVAs are designed on inexpensive FR4 substrate.The AVA with non-uniform corrugation has compact dimension of 50×50 mm2 or 0.28λL×0.28λL,whereλL is wavelength of the lowest operating frequency.The antenna can operate within the frequency range from 1.63 GHz to over 8 GHz.For the AVA with parasitic patch and uniform corrugation,the overall size of antenna is 50×86 mm2 or 0.24λL×0.41λL.It can operate within the frequency range from 1.4 GHz to over 8 GHz.The maximum gain for AVA with non-uniform corrugation and AVA with parasitic patch and uniform corrugation are 9.03 and 11.31 dBi,respectively.The corrugation profile and parasitic patch of the proposed antenna are optimized to achieve the desired properties for breast cancer detection.In addition,the proposed AVAs are measured with breast phantom to detect cancerous cell inside the breast and the performance in detecting cancerous cell are discussed.The measured result can confirm that the proposed AVAs can detect unwanted cell inside the breast while maintaining the compact size,simple structure and low complexity in design.

MIoT Based Skin Cancer Detection Using Bregman Recurrent Deep Learning

Mobile clouds are the most common medium for aggregating,storing,and analyzing data from the medical Internet of Things(MIoT).It is employed to monitor a patient’s essential health signs for earlier disease diagnosis and prediction.Among the various disease,skin cancer was the wide variety of cancer,as well as enhances the endurance rate.In recent years,many skin cancer classification systems using machine and deep learning models have been developed for classifying skin tumors,including malignant melanoma(MM)and other skin cancers.However,accurate cancer detection was not performed with minimum time consumption.In order to address these existing problems,a novel Multidimensional Bregman Divergencive Feature Scaling Based Cophenetic Piecewise Regression Recurrent Deep Learning Classification(MBDFS-CPRRDLC)technique is introduced for detecting cancer at an earlier stage.The MBDFS-CPRRDLC performs skin cancer detection using different layers such as input,hidden,and output for feature selection and classification.The patient information is composed of IoT.The patient information was stored in mobile clouds server for performing predictive analytics.The collected data are sent to the recurrent deep learning classifier.In the first hidden layer,the feature selection process is carried out using the Multidimensional Bregman Divergencive Feature Scaling technique to find the significant features for disease identification resulting in decreases time consumption.Followed by,the disease classification is carried out in the second hidden layer using cophenetic correlative piecewise regression for analyzing the testing and training data.This process is repeatedly performed until the error gets minimized.In this way,disease classification is accurately performed with higher accuracy.Experimental evaluation is carried out for factors namely Accuracy,precision,recall,F-measure,as well as cancer detection time,by the amount of patient data.The observed result confirms that the proposed MBDFS-CPRRDLC technique increases accuracy as well as lesser cancer detection time compared to the conventional approaches.

Adaptive deep learning for head and neck cancer detection using hyperspectral imaging

It can be challenging to detect tumor margins during surgery for complete resection.The purpose of this work is to develop a novel learning method that learns the difference between the tumor and benign tissue adaptively for cancer detection on hyperspectral images in an animal model.Specifically,an auto-encoder network is trained based on the wavelength bands on hyperspectral images to extract the deep information to create a pixel-wise prediction of cancerous and benign pixel.According to the output hypothesis of each pixel,the misclassified pixels would be reclassified in the right prediction direction based on their adaptive weights.The auto-encoder network is again trained based on these updated pixels.The learner can adaptively improve the ability to identify the cancer and benign tissue by focusing on the misclassified pixels,and thus can improve the detection performance.The adaptive deep learning method highlighting the tumor region proved to be accurate in detecting the tumor boundary on hyperspectral images and achieved a sensitivity of 92.32%and a specificity of 91.31%in our animal experiments.This adaptive learning method on hyperspectral imaging has the potential to provide a noninvasive tool for tumor detection,especially,for the tumor whose margin is indistinct and irregular.

Abnormal DNA methylation as a cell-free circulating DNA biomarker for colorectal cancer detection:A review of literature

Colorectal cancer(CRC) is one of the most prevalent malignancies in the world. CRC-associated morbidity and mortality is continuously increasing, in part due to a lack of early detection. The existing screening tools such as colonoscopy, are invasive and yet high cost, affecting the willingness of patients to participate in screening programs. In recent years, evidence is accumulating that the interaction of aberrant genetic and epigenetic modifications is the cornerstone for the CRC development and progression by alternating the function of tumor suppressor genes, DNA repair genes and oncogenes of colonic cells. Apart from the understanding of the underlying mechanism(s) of carcinogenesis, the aforementioned interaction has also allowed identification of clinical biomarkers, especially epigenetic, for the early detection and prognosis of cancer patients. One of the ways to detect these epigenetic biomarkers is the cell-free circulating DNA(circ DNA), a blood-based cancer diagnostic test, mainly focusing in the molecular alterations found in tumor cells, such as DNA mutations and DNA methylation.In this brief review, we epitomize the current knowledge on the research in circ DNA biomarkers-mainly focusing on DNA methylation-as potential blood-based tests for early detection of colorectal cancer and the challenges for validation and globally implementation of this emergent technology.

Enhancing Early Detection of Lung Cancer through Advanced Image Processing Techniques and Deep Learning Architectures for CT Scans

Lung cancer remains a major concern in modern oncology due to its high mortality rates and multifaceted origins,including hereditary factors and various clinical changes.It stands as the deadliest type of cancer and a significant cause of cancer-related deaths globally.Early diagnosis enables healthcare providers to administer appropriate treatment measures promptly and accurately,leading to improved prognosis and higher survival rates.The significant increase in both the incidence and mortality rates of lung cancer,particularly its ranking as the second most prevalent cancer among women worldwide,underscores the need for comprehensive research into efficient screening methods.Advances in diagnostic techniques,particularly the use of computed tomography(CT)scans,have revolutionized the identification of lung cancer.CT scans are renowned for their ability to provide high-resolution images and are particularly effective in detecting small,calcified areas,crucial for identifying earlystage lung cancer.Consequently,there is growing interest in enhancing computer-aided detection(CAD)systems.These algorithms assist radiologists by reducing false-positive interpretations and improving the accuracy of early cancer diagnosis.This study aims to enhance the effectiveness of CAD systems through various methods.Initially,the Contrast Limited Adaptive Histogram Equalization(CLAHE)algorithm is employed to preprocess CT scan images,thereby improving their visual quality.Further refinement is achieved by integrating different optimization strategies with the CLAHE method.The CutMix data augmentation technique is applied to boost the performance of the proposed model.A comparative analysis is conducted using deep learning architectures such as InceptionV3,ResNet101,Xception,and EfficientNet.The study evaluates the performance of these architectures in image classification tasks,both with and without the implementation of the CLAHE algorithm.The empirical findings of the study demonstrate a significant reduction in the false positive rate(FPR)and an overall enhancement in diagnostic accuracy.This research not only contributes to the field of medical imaging but also holds significant implications for the early detection and treatment of lung cancer,ultimately aiming to reduce its mortality rates.

Transrectal versus transperineal prostate biopsy for cancer detection in patients with gray-zone prostatespecific antigen:a multicenter,real-world study

Knowledge about the effect of different prostate biopsy approaches on the prostate cancer detection rate(CDR)in patients with gray-zone prostate-specific antigen(PSA)is limited.We performed this study to compare the CDR among patients who underwent different biopsy approaches and had rising PSA levels in the gray zone.Two hundred and twenty-two patients who underwent transrectal prostate biopsy(TRB)and 216 patients who underwent transperineal prostate biopsy(TPB)between June 2016 and September 2022 were reviewed in this study.In addition,110 patients who received additional targeted biopsies following the systematic TPB were identified.Clinical parameters,including age,PSA derivative,prostate volume(PV),and needle core count,were recorded.The data were fitted via propensity score matching(PSM),adjusting for potential confounders.TPB outperformed TRB in terms of the CDR(49.6%vs 28.3%,P=0.001).The clinically significant prostate cancer(csPCa)detection rate was not significantly different between TPB and TRB(78.6%vs 68.8%,P=0.306).In stratified analysis,TPB outperformed TRB in CDR when the age of patients was 65–75 years(59.0%vs 22.0%,P<0.001),when PV was 25.00–50.00 ml(63.2%vs 28.3%,P<0.001),and when needle core count was no more than 12(58.5%vs 31.5%,P=0.005).The CDR(P=0.712)and detection rate of csPCa(P=0.993)did not significantly differ among the systematic,targeted,and combined biopsies.TPB outperformed TRB in CDR for patients with gray-zone PSA.Moreover,performing target biopsy after systematic TPB provided no additional benefits in CDR.

An ultra-sensitive metasurface biosensor for instant cancer detection based on terahertz spectra

Metasurface biosensors have become the core label-free and rapid-detection technology in bioanalysis.Lung cancer and brain cancer are the first leading causes of cancer death among adults and adolescents,respectively,where poor early diagnosis results from expensive detection costs and time consumption.To tackle the above problems,here,we introduce a terahertzdomain metasurface biosensor for cancer diagnosis,relying on a perfectly symmetrical periodic surface structure,which significantly exhibits polarization-insensitivity at 2.05 THz and the high-sensitivity of 504 GHz/RIU(RIU=refractive index unit).According to the frequency shifts and transmittance variations,four cell types are successfully distinguished from each other.The minimum number of cells is required for thousands of cells to display the differences of spectra,which is 1/30 of clinical methods.Furthermore,the results were consistent with pathological results(the gold standard in clinic)by Gaussian curve fitting.The proposed biosensor has really achieved the characterization of cells in normal and cancerous state.This detection strategy dramatically reduced the cost of detection by reuse and time consumption was reduced to 1/20 of the pathology testing.In addition,it is flexible to set samples and easy to realize automatic operation due to the great polarization-insensitivity of the proposed biosensor,which can further reduce labor costs in the future.It is envisioned that the proposed biosensor will present immense potential in the fields of cancer detection,distinguishing different cancers,identifying primary lesion cancer or metastatic cancer.

Advancements in Barrett's esophagus detection:The role of artificial intelligence and its implications

Artificial intelligence(AI)is making significant strides in revolutionizing the detection of Barrett's esophagus(BE),a precursor to esophageal adenocarcinoma.In the research article by Tsai et al,researchers utilized endoscopic images to train an AI model,challenging the traditional distinction between endoscopic and histological BE.This approach yielded remarkable results,with the AI system achieving an accuracy of 94.37%,sensitivity of 94.29%,and specificity of 94.44%.The study's extensive dataset enhances the AI model's practicality,offering valuable support to endoscopists by minimizing unnecessary biopsies.However,questions about the applicability to different endoscopic systems remain.The study underscores the potential of AI in BE detection while highlighting the need for further research to assess its adaptability to diverse clinical settings.

Prediction of Lung Cancer Stage Using Tumor Gene Expression Data

Lung cancer remains a significant global health challenge and identifying lung cancer at an early stage is essential for enhancing patient outcomes. The study focuses on developing and optimizing gene expression-based models for classifying cancer types using machine learning techniques. By applying Log2 normalization to gene expression data and conducting Wilcoxon rank sum tests, the researchers employed various classifiers and Incremental Feature Selection (IFS) strategies. The study culminated in two optimized models using the XGBoost classifier, comprising 10 and 74 genes respectively. The 10-gene model, due to its simplicity, is proposed for easier clinical implementation, whereas the 74-gene model exhibited superior performance in terms of Specificity, AUC (Area Under the Curve), and Precision. These models were evaluated based on their sensitivity, AUC, and specificity, aiming to achieve high sensitivity and AUC while maintaining reasonable specificity.

Novel automated non-invasive detection of ocular surface squamous neoplasia using artificial intelligence

Ocular surface squamous neoplasia(OSSN)is a common eye surface tumour,characterized by the growth of abnormal cells on the ocular surface.OSSN includes invasive squamous cell carcinoma(SCC),in which tumour cells penetrate the basement membrane and infiltrate the stroma,as well as non-invasive conjunctival intraepithelial neoplasia,dysplasia,and SCC in-situ thereby presenting a challenge in early detection and diagnosis.Early identification and precise demarcation of the OSSN border leads to straightforward and curative treatments,such as topical medicines,whereas advanced invasive lesions may need orbital exenteration,which carries a risk of death.Artificial intelligence(AI)has emerged as a promising tool in the field of eye care and holds potential for its application in OSSN management.AI algorithms trained on large datasets can analyze ocular surface images to identify suspicious lesions associated with OSSN,aiding ophthalmologists in early detection and diagnosis.AI can also track and monitor lesion progression over time,providing objective measurements to guide treatment decisions.Furthermore,AI can assist in treatment planning by offering personalized recommendations based on patient data and predicting the treatment response.This manuscript highlights the role of AI in OSSN,specifically focusing on its contributions in early detection and diagnosis,assessment of lesion progression,treatment planning,telemedicine and remote monitoring,and research and data analysis.

The outpost against cancer:universal cancer only markers

Cancer is the leading cause of death worldwide.Early detection of cancer can lower the mortality of all types of cancer;however,effective early-detection biomarkers are lacking for most types of cancers.DNA methylation has always been a major target of interest because DNA methylation usually occurs before other detectable genetic changes.While investigating the common features of cancer using a novel guide positioning sequencing for DNA methylation,a series of universal cancer only markers(UCOMs)have emerged as strong candidates for effective and accurate early detection of cancer.While the clinical value of current cancer biomarkers is diminished by low sensitivity and/or low specificity,the unique characteristics of UCOMs ensure clinically meaningful results.Validation of the clinical potential of UCOMs in lung,cervical,endometrial,and urothelial cancers further supports the application of UCOMs in multiple cancer types and various clinical scenarios.In fact,the applications of UCOMs are currently under active investigation with further evaluation in the early detection of cancer,auxiliary diagnosis,treatment efficacy,and recurrence monitoring.The molecular mechanisms by which UCOMs detect cancers are the next important topics to be investigated.The application of UCOMs in real-world scenarios also requires implementation and refinement.