Methods Used in Computer-Aided Diagnosis for Breast Cancer Detection Using Mammograms: A Review

Breast Cancer Screening Using Inverse Modeling of Surface Temperatures and Steady-State Thermal Imaging

Cancer is characterized by increased metabolic activity and vascularity, leading to temperature changes in cancerous tissues compared to normal cells. This study focused on patients with abnormal mammogram findings or a clinical suspicion of breast cancer, exclusively those confirmed by biopsy. Utilizing an ultra-high sensitivity thermal camera and prone patient positioning, we measured surface temperatures integrated with an inverse modeling technique based on heat transfer principles to predict malignant breast lesions. Involving 25 breast tumors, our technique accurately predicted all tumors, with maximum errors below 5 mm in size and less than 1 cm in tumor location. Predictive efficacy was unaffected by tumor size, location, or breast density, with no aberrant predictions in the contralateral normal breast. Infrared temperature profiles and inverse modeling using both techniques successfully predicted breast cancer, highlighting its potential in breast cancer screening.

A Narrative Review of the Use of Artificial Intelligence in Breast, Lung, and Prostate Cancer

Artificial intelligence (AI) has been an important topic within radiology. Currently, AI is used clinically to assist with the detection of lesions through detection systems. However, a number of recent studies have demonstrated the increased value of neural networks in radiology. With an increasing number of screening requirements for cancers, this review aims to study the accuracy of the numerous AI models used in the detection and diagnosis of breast, lung, and prostate cancers. This study summarizes pertinent findings from reviewed articles and provides analysis on the relevancy to clinical radiology. This study found that whereas AI is showing continual improvement in radiology, AI alone does not surpass the effectiveness of a radiologist. Additionally, it was found that there are multiple variations on how AI should be integrated with a radiologist's workflow.

Can Artificial Intelligence Beat Humans in Detecting Breast Malignancy on Mammograms?

BACKGROUND

The study was aimed at identifying how useful Computer-Aided Detection (CAD) could be in reducing false-negative reporting in mammography and early detection of breast cancer at an early stage as the best protection is early detection.

MATERIALS AND METHODS

This retrospective study was conducted in a tertiary care setup of Atomic Energy Cancer Hospital, Nuclear Medicine, Oncology and Radiotherapy Institute (AECH-NORI), where 33 patients with suspicious findings on mammography and subsequent biopsy-proven malignancy were included. The findings of mammography including the lesion type, breast parenchymal density, and sensitivity of CAD detection, as well as the final biopsy results, were recorded. A second group of 40 normal screening mammograms was also included who had no symptoms, had Breast Imaging-Reporting and Data System category I(BI-RADS I) mammograms, and had no pathology identified on correlative sonomammography as well.

RESULTS

A total of 35 masses, 11 pleomorphic clusters of microcalcification, five clustered foci of macrocalcification, and nine lesions with pleomorphic clusters of microcalcification and two with pleomorphic clusters of microcalcification only were included. The CAD system was able to identify 26 masses (74%), eight lesions with pleomorphic clusters of microcalcification (72%), five foci of macrocalcification (100%), six lesions with pleomorphic clusters of microcalcification (66%), and two pleomorphic clusters of microcalcification without formed mass (100%). The overall sensitivity of the CAD system was 75.8%. CAD was able to identify 13 out of 16 masses with invasive ductal carcinoma (81.3%), eight out of nine lesions proven as invasive ductal carcinoma with ductal carcinoma in situ (DCIS) (88.9%), two out of five masses with invasive lobular carcinoma (40%), four out of four masses with invasive mammary carcinoma (100%), and zero out of one lesion identified as medullary carcinoma (0%). There was 100% detection for pleomorphic clusters of microcalcification without formed mass with CAD marking two out of two mammograms.

CONCLUSION

CAD performed better with combined lesions, accurately marked pleomorphic clusters of microcalcification, and identified small lesions in predominant fibrofatty parenchymal density but was not reliable in dense breast, areas of asymmetric increased density, summation artifacts, edematous breast parenchyma, and retroareolar lesions. It also performed poorly with ill-defined lesions of invasive lobular carcinoma. Human intelligence hence beats CAD for the diagnosis of breast malignancy in mammograms as per our experience.

Classification of Breast Cancer Using Transfer Learning and Advanced Al-Biruni Earth Radius Optimization

Breast cancer is one of the most common cancers in women, with an estimated 287,850 new cases identified in 2022. There were 43,250 female deaths attributed to this malignancy. The high death rate associated with this type of cancer can be reduced with early detection. Nonetheless, a skilled professional is always necessary to manually diagnose this malignancy from mammography images. Many researchers have proposed several approaches based on artificial intelligence. However, they still face several obstacles, such as overlapping cancerous and noncancerous regions, extracting irrelevant features, and inadequate training models. In this paper, we developed a novel computationally automated biological mechanism for categorizing breast cancer. Using a new optimization approach based on the Advanced Al-Biruni Earth Radius (ABER) optimization algorithm, a boosting to the classification of breast cancer cases is realized. The stages of the proposed framework include data augmentation, feature extraction using AlexNet based on transfer learning, and optimized classification using a convolutional neural network (CNN). Using transfer learning and optimized CNN for classification improved the accuracy when the results are compared to recent approaches. Two publicly available datasets are utilized to evaluate the proposed framework, and the average classification accuracy is 97.95%. To ensure the statistical significance and difference between the proposed methodology, additional tests are conducted, such as analysis of variance (ANOVA) and Wilcoxon, in addition to evaluating various statistical analysis metrics. The results of these tests emphasized the effectiveness and statistical difference of the proposed methodology compared to current methods.

Vaccines in Breast Cancer: Challenges and Breakthroughs

Breast cancer is a problem for women's health globally. Early detection techniques come in a variety of forms ranging from local to systemic and from non-invasive to invasive. The treatment of cancer has always been challenging despite the availability of a wide range of therapeutics. This is either due to the variable behaviour and heterogeneity of the proliferating cells and/or the individual's response towards the treatment applied. However, advancements in cancer biology and scientific technology have changed the course of the cancer treatment approach. This current review briefly encompasses the diagnostics, the latest and most recent breakthrough strategies and challenges, and the limitations in fighting breast cancer, emphasising the development of breast cancer vaccines. It also includes the filed/granted patents referring to the same aspects.

Persistent Homology-Based Machine Learning Method for Filtering and Classifying Mammographic Microcalcification Images in Early Cancer Detection

Microcalcifications in mammogram images are primary indicators for detecting the early stages of breast cancer. However, dense tissues and noise in the images make it challenging to classify the microcalcifications. Currently, preprocessing procedures such as noise removal techniques are applied directly on the images, which may produce a blurry effect and loss of image details. Further, most of the features used in classification models focus on local information of the images and are often burdened with details, resulting in data complexity. This research proposed a filtering and feature extraction technique using persistent homology (PH), a powerful mathematical tool used to study the structure of complex datasets and patterns. The filtering process is not performed directly on the image matrix but through the diagrams arising from PH. These diagrams will enable us to distinguish prominent characteristics of the image from noise. The filtered diagrams are then vectorised using PH features. Supervised machine learning models are trained on the MIAS and DDSM datasets to evaluate the extracted features' efficacy in discriminating between benign and malignant classes and to obtain the optimal filtering level. This study reveals that appropriate PH filtering levels and features can improve classification accuracy in early cancer detection.

BC2NetRF: Breast Cancer Classification from Mammogram Images Using Enhanced Deep Learning Features and Equilibrium-Jaya Controlled Regula Falsi-Based Features Selection

One of the most frequent cancers in women is breast cancer, and in the year 2022, approximately 287,850 new cases have been diagnosed. From them, 43,250 women died from this cancer. An early diagnosis of this cancer can help to overcome the mortality rate. However, the manual diagnosis of this cancer using mammogram images is not an easy process and always requires an expert person. Several AI-based techniques have been suggested in the literature. However, still, they are facing several challenges, such as similarities between cancer and non-cancer regions, irrelevant feature extraction, and weak training models. In this work, we proposed a new automated computerized framework for breast cancer classification. The proposed framework improves the contrast using a novel enhancement technique called haze-reduced local-global. The enhanced images are later employed for the dataset augmentation. This step aimed at increasing the diversity of the dataset and improving the training capability of the selected deep learning model. After that, a pre-trained model named EfficientNet-b0 was employed and fine-tuned to add a few new layers. The fine-tuned model was trained separately on original and enhanced images using deep transfer learning concepts with static hyperparameters’ initialization. Deep features were extracted from the average pooling layer in the next step and fused using a new serial-based approach. The fused features were later optimized using a feature selection algorithm known as Equilibrium-Jaya controlled Regula Falsi. The Regula Falsi was employed as a termination function in this algorithm. The selected features were finally classified using several machine learning classifiers. The experimental process was conducted on two publicly available datasets—CBIS-DDSM and INbreast. For these datasets, the achieved average accuracy is 95.4% and 99.7%. A comparison with state-of-the-art (SOTA) technology shows that the obtained proposed framework improved the accuracy. Moreover, the confidence interval-based analysis shows consistent results of the proposed framework.

An Efficient USE-Net Deep Learning Model for Cancer Detection

Breast cancer (BrCa) is the most common disease in women worldwide. Classifying the BrCa image is extremely important for finding BrCa at an earlier stage and monitoring BrCa during treatment. The computer-aided detection methods have been used to interpret BrCa and improve the detection of BrCa during the screening and treatment stages. However, if a new BrCa image is generated for the treatment, it will not classify correctly. The main objective of this research is to classify the BrCa images for newly generated images. The model performs preprocessing, segmentation, feature extraction, and classification. In preprocessing, a hybrid median filtering (HMF) is used to eliminate the noise in the images. The contrast of the images is enhanced using quadrant dynamic histogram equalization (QDHE). Then, ROI segmentation is performed using the USE-Net deep learning model. The CaffeNet model is used for feature extraction on the segmented images, and finally, classification is made using the improved random forest (IRF) with extreme gradient boosting (XGB). The model obtained 97.87% accuracy, 98.45% sensitivity, 95.24% specificity, 98.96% precision, and 98.70% f1-score for ultrasound images. The model gives 98.31% accuracy, 99.29% sensitivity, 90.20% specificity, 98.82% precision, and 99.05% f1-score for mammogram images.

Computer-aided breast cancer detection and classification in mammography: A comprehensive review

Cancer is the second cause of mortality worldwide and it has been identified as a perilous disease. Breast cancer accounts for ∼20% of all new cancer cases worldwide, making it a major cause of morbidity and mortality. Mammography is an effective screening tool for the early detection and management of breast cancer. However, the identification and interpretation of breast lesions is challenging even for expert radiologists. For that reason, several Computer-Aided Diagnosis (CAD) systems are being developed to assist radiologists to accurately detect and/or classify breast cancer. This review examines the recent literature on the automatic detection and/or classification of breast cancer in mammograms, using both conventional feature-based machine learning and deep learning algorithms. The review begins with a comparison of algorithms developed specifically for the detection and/or classification of two types of breast abnormalities, micro-calcifications and masses, followed by the use of sequential mammograms for improving the performance of the algorithms. The available Food and Drug Administration (FDA) approved CAD systems related to triage and diagnosis of breast cancer in mammograms are subsequently presented. Finally, a description of the open access mammography datasets is provided and the potential opportunities for future work in this field are highlighted. The comprehensive review provided here can serve both as a thorough introduction to the field but also provide indicative directions to guide future applications.

Exploring different computational approaches for effective diagnosis of breast cancer

Breast cancer has been identified as one among the top causes of female death worldwide. According to recent research, earlier detection plays an important role toward fortunate medicaments and thus, decreasing the mortality rate due to breast cancer among females. This review provides a fleeting summary involving traditional diagnostic procedures from the past and today, and also modern computational tools that have greatly aided in the identification of breast cancer. Computational techniques involving different algorithms such as Support vector machines, deep learning techniques and robotics are popular among the academicians for detection of breast cancer. They discovered that Convolutional neural network was a common option for categorization among such approaches. Deep learning techniques are evaluated using performance indicators such as accuracy, sensitivity, specificity, or measure. Furthermore, molecular docking, homology modeling and Molecular dynamics Simulation gives a road map for future discussions about developing improved early detection approaches that holds greater potential in increasing the survival rate of cancer patients. The different computational techniques can be a new dominion among researchers and combating the challenges associated with breast cancer.

A review of the current state of the computer-aided diagnosis (CAD) systems for breast cancer diagnosis

Breast cancer is one of the most common cancers affecting females worldwide. Early detection and diagnosis of breast cancer may aid in timely treatment, reducing the mortality rate to a great extent. To diagnose breast cancer, computer-aided diagnosis (CAD) systems employ a variety of imaging modalities such as mammography, computerized tomography, magnetic resonance imaging, ultrasound, and histological imaging. CAD and breast-imaging specialists are in high demand for early detection and diagnosis. This system has the potential to enhance the partiality of traditional histopathological image analysis. This review aims to highlight the recent advancements and the current state of CAD systems for breast cancer detection using different modalities.

Insight into breast cancer detection: new hybrid feature selection method

Breast cancer, which is also the leading cause of death among women, is one of the most common forms of the disease that affects females all over the world. The discovery of breast cancer at an early stage is extremely important because it allows selecting appropriate treatment protocol and thus, stops the development of cancer cells. In this paper, a new patients detection strategy has been presented to identify patients with the disease earlier. The proposed strategy composes of two parts which are data preprocessing phase and patient detection phase (PDP). The purpose of this study is to introduce a feature selection methodology for determining the most efficient and significant features for identifying breast cancer patients. This method is known as new hybrid feature selection method (NHFSM). NHFSM is made up of two modules which are quick selection module that uses information gain, and feature selection module that uses hybrid bat algorithm and particle swarm optimization. Consequently, NHFSM is a hybrid method that combines the advantages of bat algorithm and particle swarm optimization based on filter method to eliminate many drawbacks such as being stuck in a local optimal solution and having unbalanced exploitation. The preprocessed data are then used during PDP in order to enable a quick and accurate detection of patients. Based on experimental results, the proposed NHFSM improves the efficiency of patients’ classification in comparison with state-of-the-art feature selection approaches by roughly 0.97, 0.76, 0.75, and 0.716 in terms of accuracy, precision, sensitivity/recall, and F-measure. In contrast, it has the lowest error rate value of 0.03.

Radiomics for Discriminating Benign and Malignant Salivary Gland Tumors; Which Radiomic Feature Categories and MRI Sequences Should Be Used?

The lack of a consistent MRI radiomic signature, partly due to the multitude of initial feature analyses, limits the widespread clinical application of radiomics for the discrimination of salivary gland tumors (SGTs). This study aimed to identify the optimal radiomics feature category and MRI sequence for characterizing SGTs, which could serve as a step towards obtaining a consensus on a radiomics signature. Preliminary radiomics models were built to discriminate malignant SGTs (n = 34) from benign SGTs (n = 57) on T1-weighted (T1WI), fat-suppressed (FS)-T2WI and contrast-enhanced (CE)-T1WI images using six feature categories. The discrimination performances of these preliminary models were evaluated using 5-fold-cross-validation with 100 repetitions and the area under the receiver operating characteristic curve (AUC). The differences between models’ performances were identified using one-way ANOVA. Results show that the best feature categories were logarithm for T1WI and CE-T1WI and exponential for FS-T2WI, with AUCs of 0.828, 0.754 and 0.819, respectively. These AUCs were higher than the AUCs obtained using all feature categories combined, which were 0.750, 0.707 and 0.774, respectively (p < 0.001). The highest AUC (0.846) was obtained using a combination of T1WI + logarithm and FS-T2WI + exponential features, which reduced the initial features by 94.0% (from 1015 × 3 to 91 × 2). CE-T1WI did not improve performance. Using one feature category rather than all feature categories combined reduced the number of initial features without compromising radiomic performance.

Classification of Breast Cancer Histopathological Images Using DenseNet and Transfer Learning

Breast cancer is one of the most common invading cancers in women. Analyzing breast cancer is nontrivial and may lead to disagreements among experts. Although deep learning methods achieved an excellent performance in classification tasks including breast cancer histopathological images, the existing state-of-the-art methods are computationally expensive and may overfit due to extracting features from in-distribution images. In this paper, our contribution is mainly twofold. First, we perform a short survey on deep-learning-based models for classifying histopathological images to investigate the most popular and optimized training-testing ratios. Our findings reveal that the most popular training-testing ratio for histopathological image classification is 70%: 30%, whereas the best performance (e.g., accuracy) is achieved by using the training-testing ratio of 80%: 20% on an identical dataset. Second, we propose a method named DenTnet to classify breast cancer histopathological images chiefly. DenTnet utilizes the principle of transfer learning to solve the problem of extracting features from the same distribution using DenseNet as a backbone model. The proposed DenTnet method is shown to be superior in comparison to a number of leading deep learning methods in terms of detection accuracy (up to 99.28% on BreaKHis dataset deeming training-testing ratio of 80%: 20%) with good generalization ability and computational speed. The limitation of existing methods including the requirement of high computation and utilization of the same feature distribution is mitigated by dint of the DenTnet.

A Diagnostic Model of Breast Cancer Based on Digital Mammogram Images Using Machine Learning Techniques

Breast cancer disease is one of the most recorded cancers that lead to morbidity and maybe death among women around the world. Recent research statistics have exposed that one from 8 females in the USA and one from 10 females in Europe are contaminated by breast cancer. The challenge with this disease is how to develop a relaxed and fast diagnosing method. One of the attractive ways of early breast cancer diagnosis is based on the mammogram images analysis of the breast using a computer-aided diagnosing (CAD) tool. This paper firstly aimed to propose an efficient method for diagnosing tumors based on mammogram images of breasts using a machine learning approach. Secondly, this paper aimed to the development of a CAD software program for breast cancer diagnosing based on the proposed method in the first step. The followed step-by-step procedure of the proposed method is performed by passing the Mammographic Image Analysis Society (MIAS) through five steps of image preprocessing, image segmentation using seeded region growing (SRG) algorithm, feature extraction using different feature’s extraction classes, and important and effectiveness feature selection using the Sequential Forward Selection (SFS) technique, and finally, the Support Vector Machine (SVM) algorithm is used as a binary classifier in two classification levels. The first level classifier is used to categorize the given image as normal or abnormal while the second-level classifier is used for further classifying the abnormal image as either a malignant or benign cancer. The proposed method is studied and investigated in two phases: the training phase and the testing phase, with the MIAS dataset of mammogram images, using 70% and 30% ratios of dataset images for the training and testing sets, respectively. The practical implementation of the proposed method and the graphical user interface (GUI) CAD tool are carried out using MATLAB software. Experimental results of the proposed method have shown that the accuracy of the proposed method reached 100% in classifying images as normal and abnormal mammogram images while the classification accuracy for benign and malignant is equal to 87.1%.

An integrated framework for breast mass classification and diagnosis using stacked ensemble of residual neural networks

A computer-aided diagnosis (CAD) system requires automated stages of tumor detection, segmentation, and classification that are integrated sequentially into one framework to assist the radiologists with a final diagnosis decision. In this paper, we introduce the final step of breast mass classification and diagnosis using a stacked ensemble of residual neural network (ResNet) models (i.e. ResNet50V2, ResNet101V2, and ResNet152V2). The work presents the task of classifying the detected and segmented breast masses into malignant or benign, and diagnosing the Breast Imaging Reporting and Data System (BI-RADS) assessment category with a score from 2 to 6 and the shape as oval, round, lobulated, or irregular. The proposed methodology was evaluated on two publicly available datasets, the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM) and INbreast, and additionally on a private dataset. Comparative experiments were conducted on the individual models and an average ensemble of models with an XGBoost classifier. Qualitative and quantitative results show that the proposed model achieved better performance for (1) Pathology classification with an accuracy of 95.13%, 99.20%, and 95.88%; (2) BI-RADS category classification with an accuracy of 85.38%, 99%, and 96.08% respectively on CBIS-DDSM, INbreast, and the private dataset; and (3) shape classification with 90.02% on the CBIS-DDSM dataset. Our results demonstrate that our proposed integrated framework could benefit from all automated stages to outperform the latest deep learning methodologies.

Unsupervised Hierarchical Classification Approach for Imprecise Data in the Breast Cancer Detection

(1) Background: in recent years, a lot of the research of statistical methods focused on the classification problem in presence of imprecise data. A particular case of imprecise data is the interval-valued data. Following this research line, in this work a new hierarchical classification technique for multivariate interval-valued data is suggested for diagnosis of the breast cancer; (2) Methods: an unsupervised hierarchical classification method for imprecise multivariate data (called HC-ID) is performed for diagnosis of breast cancer (i.e., to discriminate between benign or malignant masses) and the results have been compared with the conventional (unsupervised) hierarchical classification approach (HC); (3) Results: the application on real data shows that the HC-ID procedure performs better HC procedure in terms of accuracy (HC-ID = 0.80, HC = 0.66) and sensitivity (HC-ID = 0.61, HC = 0.08). In the results obtained by the usual procedure, there is a high degree of false-negative (i.e., benign cancer diagnosis in malignant status) affected by the high degree of variability (i.e., uncertainty) characterizing the worst data.

Deep learning-based automatic segmentation of images in cardiac radiography: A promising challenge

BACKGROUND

Due to the advancement of medical imaging and computer technology, machine intelligence to analyze clinical image data increases the probability of disease prevention and successful treatment. When diagnosing and detecting heart disease, medical imaging can provide high-resolution scans of every organ or tissue in the heart. The diagnostic results obtained by the imaging method are less susceptible to human interference. They can process numerous patient information, assist doctors in early detection of heart disease, intervene and treat patients, and improve the understanding of heart disease symptoms and clinical diagnosis of great significance. In a computer-aided diagnosis system, accurate segmentation of cardiac scan images is the basis and premise of subsequent thoracic function analysis and 3D image reconstruction.

EXISTING TECHNIQUES

This paper systematically reviews automatic methods and some difficulties for cardiac segmentation in radiographic images. Combined with recent advanced deep learning techniques, the feasibility of using deep learning network models for image segmentation is discussed, and the commonly used deep learning frameworks are compared.

DEVELOPED INSIGHTS

There are many standard methods for medical image segmentation, such as traditional methods based on regions and edges and methods based on deep learning. Because of characteristics of non-uniform grayscale, individual differences, artifacts and noise of medical images, the above image segmentation methods have certain limitations. It is tough to obtain the needed results sensitivity and accuracy when performing heart segmentation. The deep learning model proposed has achieved good results in image segmentation. Accurate segmentation improves the accuracy of disease diagnosis and reduces subsequent irrelevant computations.

SUMMARY

There are two requirements for accurate segmentation of radiological images. One is to use image segmentation to improve the development of computer-aided diagnosis. The other is to achieve complete segmentation of the heart. When there are lesions or deformities in the heart, there will be some abnormalities in the radiographic images, and the segmentation algorithm needs to segment the heart altogether. The quantity of processing inside a certain range will no longer be a restriction for real-time detection with the advancement of deep learning and the enhancement of hardware device performance.

Breast Cancer Detection Using Mammogram Images with Improved Multi-Fractal Dimension Approach and Feature Fusion

Breast cancer detection using mammogram images at an early stage is an important step in disease diagnostics. We propose a new method for the classification of benign or malignant breast cancer from mammogram images. Hybrid thresholding and the machine learning method are used to derive the region of interest (ROI). The derived ROI is then separated into five different blocks. The wavelet transform is applied to suppress noise from each produced block based on BayesShrink soft thresholding by capturing high and low frequencies within different sub-bands. An improved fractal dimension (FD) approach, called multi-FD (M-FD), is proposed to extract multiple features from each denoised block. The number of features extracted is then reduced by a genetic algorithm. Five classifiers are trained and used with the artificial neural network (ANN) to classify the extracted features from each block. Lastly, the fusion process is performed on the results of five blocks to obtain the final decision. The proposed approach is tested and evaluated on four benchmark mammogram image datasets (MIAS, DDSM, INbreast, and BCDR). We present the results of single- and double-dataset evaluations. Only one dataset is used for training and testing in the single-dataset evaluation, whereas two datasets (one for training, and one for testing) are used in the double-dataset evaluation. The experiment results show that the proposed method yields better results on the INbreast dataset in the single-dataset evaluation, whilst better results are obtained on the remaining datasets in the double-dataset evaluation. The proposed approach outperforms other state-of-the-art models on the Mini-MIAS dataset.

Enhancing reasoning through reduction of vagueness using fuzzy OWL-2 for representation of breast cancer ontologies

The need to address the challenge of vagueness across several domains of applicability of ontology is gaining research attention. The presence of vagueness in knowledge represented with description logic impairs automating reasoning and inference making. The importance of reducing this vagueness in the formalization of medical knowledge representation is rising, considering the vulnerability of this domain to the expression of vague concepts or terms. This vagueness may be addressed from the perspective of ontology modeling language application such as ontology web language (OWL). Although several attempts have been made to tackle this problem in other disease prognoses such as diabetes and cardiovascular diseases, a similar effort is missing for breast cancer. Minimizing vagueness in breast cancer ontology is necessary to enhance automated reasoning and handle knowledge representation problems. This study proposes a framework for reducing vagueness in breast cancer ontology. The approach obtained breast cancer crisp ontology and applied fuzzy ontology elements based on the Fuzzy OWL2 model to formulate breast cancer fuzzy ontology. This was achieved by extending the elements of OWL2 (a more expressive version of OWL) with annotation properties to fuzzify the breast cancer crisp ontology. Results obtained showed a significant reduction of vagueness in the domain, yielding 0.38 for vagueness spread and 1.0 for vagueness explicitness. In addition, ontology metrics such as completeness, consistency, correctness and accuracy were also evaluated, and we obtained impressive performance. The implication of this result is the reduction of vagueness in breast cancer ontology, which provides increased computational reasoning support to applications using the ontology.

A Systematic Review of Artificial Intelligence Techniques in Cancer Prediction and Diagnosis

Artificial intelligence has aided in the advancement of healthcare research. The availability of open-source healthcare statistics has prompted researchers to create applications that aid cancer detection and prognosis. Deep learning and machine learning models provide a reliable, rapid, and effective solution to deal with such challenging diseases in these circumstances. PRISMA guidelines had been used to select the articles published on the web of science, EBSCO, and EMBASE between 2009 and 2021. In this study, we performed an efficient search and included the research articles that employed AI-based learning approaches for cancer prediction. A total of 185 papers are considered impactful for cancer prediction using conventional machine and deep learning-based classifications. In addition, the survey also deliberated the work done by the different researchers and highlighted the limitations of the existing literature, and performed the comparison using various parameters such as prediction rate, accuracy, sensitivity, specificity, dice score, detection rate, area undercover, precision, recall, and F1-score. Five investigations have been designed, and solutions to those were explored. Although multiple techniques recommended in the literature have achieved great prediction results, still cancer mortality has not been reduced. Thus, more extensive research to deal with the challenges in the area of cancer prediction is required.

Basic Pan-Cancer Analysis of the Carcinogenic Effects of Cyclin-Dependent Kinase 4 (CDK4) in Human Surface Tumors

Although the evidence based on current human, animal, or molecular biology can explain some of the relationships between CDK4 and cancer, there is no pan-cancer analysis of the gene CDK4 in human skin tumors. Therefore, the potential carcinogenic effects of CDK4 in 33 tumors were initially explored in the datasets of the GEO (Gene Expression Omnibus) and the CGA (Cancer Genome Atlas). We found that CDK4 was highly expressed in most cancers and that CDK4 performance levels significantly correlated with the prognosis of cancer patients. These were found in our preliminary exploration. In addition, we used the dataset in tumors such as cutaneous melanoma or lung adenocarcinoma and found increased levels of phosphorylation of r24 l/C/h/s. In addition, fibroblast infiltration associated with CDK4 cancer was observed in head and neck, sarcoma, and melanoma skin. Using this pan-cancer study, our group has provided a comprehensive preliminary demonstration of the oncogenic effects of the CDK4 gene on different human skin tumors.

Future of biomarker evaluation in the realm of artificial intelligence algorithms: application in improved therapeutic stratification of patients with breast and prostate cancer

Clinical workflows in oncology depend on predictive and prognostic biomarkers. However, the growing number of complex biomarkers contributes to costly and delayed decision-making in routine oncology care and treatment. As cancer is expected to rank as the leading cause of death and the single most important barrier to increasing life expectancy in the 21st century, there is a major emphasis on precision medicine, particularly individualisation of treatment through better prediction of patient outcome. Over the past few years, both surgical and pathology specialties have suffered cutbacks and a low uptake of pathology specialists means a solution is required to enable high-throughput screening and personalised treatment in this area to alleviate bottlenecks. Digital imaging in pathology has undergone an exponential period of growth. Deep-learning (DL) platforms for hematoxylin and eosin (H&E) image analysis, with preliminary artificial intelligence (AI)-based grading capabilities of specimens, can evaluate image characteristics which may not be visually apparent to a pathologist and offer new possibilities for better modelling of disease appearance and possibly improve the prediction of disease stage and patient outcome. Although digital pathology and AI are still emerging areas, they are the critical components for advancing personalised medicine. Integration of transcriptomic analysis, clinical information and AI-based image analysis is yet an uncultivated field by which healthcare professionals can make improved treatment decisions in cancer. This short review describes the potential application of integrative AI in offering better detection, quantification, classification, prognosis and prediction of breast and prostate cancer and also highlights the utilisation of machine learning systems in biomarker evaluation.

Classification of Chronic Kidney Disease in Sonography Using the GLCM and Artificial Neural Network

Chronic kidney disease (CKD) can be treated if it is detected early, but as the disease progresses, recovery becomes impossible. Eventually, renal replacement therapy such as transplantation or dialysis is necessary. Ultrasound is a test method with which to diagnose kidney cancer, inflammatory disease, nodular disease, chronic kidney disease, etc. It is used to determine the degree of inflammation using information such as the kidney size and internal echo characteristics. The degree of the progression of chronic kidney disease in the current clinical trial is based on the value of the glomerular filtration rate. However, changes in the degree of inflammation and disease can even be observed with ultrasound. In this study, from a total of 741 images, 251 normal kidney images, 328 mild and moderate CKD images, and 162 severe CKD images were tested. In order to diagnose CKD in clinical practice, three ROIs were set: the cortex of the kidney, the boundary between the cortex and medulla, and the medulla, which are areas examined to obtain information from ultrasound images. Parameters were extracted from each ROI using the GLCM algorithm, which is widely used in ultrasound image analysis. When each parameter was extracted from the three areas, a total of 57 GLCM parameters were extracted. Finally, a total of 58 parameters were used by adding information on the size of the kidney, which is important for the diagnosis of chronic kidney disease. The artificial neural network (ANN) was composed of 58 input parameters, 10 hidden layers, and 3 output layers (normal, mild and moderate CKD, and severe CKD). Using the ANN model, the final classification rate was 95.4%, the epoch needed for training was 38 times, and the misclassification rate was 4.6%.

Classification of Hematoxylin and Eosin-Stained Breast Cancer Histology Microscopy Images Using Transfer Learning with EfficientNets

Breast cancer is a fatal disease and is a leading cause of death in women worldwide. The process of diagnosis based on biopsy tissue is nontrivial, time-consuming, and prone to human error, and there may be conflict about the final diagnosis due to interobserver variability. Computer-aided diagnosis systems have been designed and implemented to combat these issues. These systems contribute significantly to increasing the efficiency and accuracy and reducing the cost of diagnosis. Moreover, these systems must perform better so that their determined diagnosis can be more reliable. This research investigates the application of the EfficientNet architecture for the classification of hematoxylin and eosin-stained breast cancer histology images provided by the ICIAR2018 dataset. Specifically, seven EfficientNets were fine-tuned and evaluated on their ability to classify images into four classes: normal, benign, in situ carcinoma, and invasive carcinoma. Moreover, two standard stain normalization techniques, Reinhard and Macenko, were observed to measure the impact of stain normalization on performance. The outcome of this approach reveals that the EfficientNet-B2 model yielded an accuracy and sensitivity of 98.33% using Reinhard stain normalization method on the training images and an accuracy and sensitivity of 96.67% using the Macenko stain normalization method. These satisfactory results indicate that transferring generic features from natural images to medical images through fine-tuning on EfficientNets can achieve satisfactory results.

A Semisupervised Learning Scheme with Self-Paced Learning for Classifying Breast Cancer Histopathological Images

The unavailability of large amounts of well-labeled data poses a significant challenge in many medical imaging tasks. Even in the likelihood of having access to sufficient data, the process of accurately labeling the data is an arduous and time-consuming one, requiring expertise skills. Again, the issue of unbalanced data further compounds the abovementioned problems and presents a considerable challenge for many machine learning algorithms. In lieu of this, the ability to develop algorithms that can exploit large amounts of unlabeled data together with a small amount of labeled data, while demonstrating robustness to data imbalance, can offer promising prospects in building highly efficient classifiers. This work proposes a semisupervised learning method that integrates self-training and self-paced learning to generate and select pseudolabeled samples for classifying breast cancer histopathological images. A novel pseudolabel generation and selection algorithm is introduced in the learning scheme to generate and select highly confident pseudolabeled samples from both well-represented classes to less-represented classes. Such a learning approach improves the performance by jointly learning a model and optimizing the generation of pseudolabels on unlabeled-target data to augment the training data and retraining the model with the generated labels. A class balancing framework that normalizes the class-wise confidence scores is also proposed to prevent the model from ignoring samples from less represented classes (hard-to-learn samples), hence effectively handling the issue of data imbalance. Extensive experimental evaluation of the proposed method on the BreakHis dataset demonstrates the effectiveness of the proposed method.

Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review

This paper provides a critical review of the literature on deep learning applications in breast tumor diagnosis using ultrasound and mammography images. It also summarizes recent advances in computer-aided diagnosis/detection (CAD) systems, which make use of new deep learning methods to automatically recognize breast images and improve the accuracy of diagnoses made by radiologists. This review is based upon published literature in the past decade (January 2010–January 2020), where we obtained around 250 research articles, and after an eligibility process, 59 articles were presented in more detail. The main findings in the classification process revealed that new DL-CAD methods are useful and effective screening tools for breast cancer, thus reducing the need for manual feature extraction. The breast tumor research community can utilize this survey as a basis for their current and future studies.

Multi-modal prediction of breast cancer using particle swarm optimization with non-dominating sorting

Cancer is enlisted as the second leading reason for death across the world wherein almost one person out of six dies of cancer. Breast cancer is one of the most common forms of cancer predominant in women having the second highest mortality rate in the world. Various scientific studies have been conducted to combat this disease, and machine learning approaches have been an extremely popular choice. Particle swarm optimization has been identified as one of the most powerful and efficient technique for the diagnosis of breast cancer guiding physicians towards timely and accurate treatment. It is also pertinent to mention that multi-modal prediction methods are used to make decisions depending upon different scenarios and aspects whereas the non-dominating sorting feature is useful to sort different objects based on differing requirements. The main novelty of this work is multi-modal prediction algorithm for breast cancer prediction is proposed. The work encompasses the use of particle swarm optimization, non-dominating sorting and multi-classifier techniques, namely, k-nearest neighbour method, fast decision tree and kernel density estimation. Finally, Bayes’ theorem is implemented for revising the results to achieve optimum accuracy in the breast cancer prediction. The proposed particle swarm optimization and non-domination sorting with classifier technique model helps to select the most significant features relevant to breast cancer predictions. The selected features design the objective of the problem model. The proposed model is implemented on the WBCD and WDBC breast cancer data sets publicly available from the UCI machine learning data repository. The metrics considered are sensitivity, specificity, accuracy and time complexity. The experimental results of the study using measures such as sensitivity, specificity, accuracy and time complexity. The experimental results of the study are evaluated against the state-of-the-art algorithms, namely, genetic algorithm kernel density estimation and particle swarm optimization kernel density estimation wherein the results justify the superiority of the proposed model.

Using Convolutional Neural Network with Cheat Sheet and Data Augmentation to Detect Breast Cancer in Mammograms

The American Cancer Society expected to diagnose 276,480 new cases of invasive breast cancer in the USA and 48,530 new cases of noninvasive breast cancer among women in 2020. Early detection of breast cancer, followed by appropriate treatment, can reduce the risk of death from this disease. DL through CNN can assist imaging specialists in classifying the mammograms accurately. Accurate classification of mammograms using CNN needs a well-trained CNN by a large number of labeled mammograms. Unfortunately, a large number of labeled mammograms are not always available. In this study, a novel procedure to aid imaging specialists in detecting normal and abnormal mammograms has been proposed. The procedure supplied the designed CNN with a cheat sheet for some classical attributes extracted from the ROI and an extra number of labeled mammograms through data augmentation. The cheat sheet aided the CNN through encoding easy-to-recognize artificial patterns in the mammogram before passing it to the CNN, and the data augmentation supported the CNN with more labeled data points. Fifteen runs of 4 different modified datasets taken from the MIAS dataset were conducted and analyzed. The results showed that the cheat sheet, along with data augmentation, enhanced CNN's accuracy by at least 12.2% and enhanced the precision of the CNN by at least 2.2. The mean accuracy, sensitivity, and specificity obtained using the proposed procedure were 92.1, 91.4, and 96.8, respectively, while the average area under the ROC curve was 94.9.