Knowledge distillation-based lightweight Convolutional Neural Networks (CNN) model for efficient breast cancer detection

Development in Computer vision applications, most prominently in Artificial Intelligence (AI) over the past decade, has encouraged researchers to use computational algorithms. Machine Learning (ML) and Deep Learning (DL) algorithms are getting attention in the biomedical area to early diagnosis and prognosis of lethiferous diseases with higher accuracy using statistical analysis. Cervical cancer is one of the predominant gynecological infectious forms of cancer in females ecumenically, generally transpiring in less-developed nations. DL algorithms are more optimized and convenient due to the automatic feature extraction in Cervical lesion detection at an early phase. Here we analyze the convolutional neural network (CNN) models' performance using variation in channels depth comprises in the convolution layers to classify multi-class liquid-based cytology (LBC) Whole slide images (WSI). We propose three CNN models having two convolution layers with the number of channels (4,8), (8,16), and (32,64) and two pooling layers. CNN model with the highest number of channels in the convolution layers gives the leading performance with an accuracy of 96.89%, precision of 93.38%, the sensitivity of 93.75%, and F-score of 94.15%.

Thousands of women worldwide are diagnosed with breast cancer yearly, which may be fatal if not treated. The diagnosis of the condition may take years, by which time the patient has little choice except to have the affected breast removed. Early diagnosis and treatments are the best ways to stop this disease's spread. In this study, the authors presented a Computer Aided Diagnosis (CAD) system to assist in breast cancer diagnosis. The study uses the Wisconsin breast cancer dataset to classify benign and malignant data. For the classification, three pre-trained Deep learning algorithms: Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Multilayer Perceptron (MLP), were used. A novel CNN model that exceeds the performance efficiency of three pre-trained models and requires minimal compilation time is proposed. A number of evaluation matrices are used to analyze the models' classification abilities. Upon closer inspection, it has been established that the proposed CNN model outperforms CNN, LSTM, and MLP models with validation accuracy of 97.85%. CNN and LSTM performed with accuracies of 94.12% with the Adagrad optimizer and 93.5% with the Adam optimizer, respectively. Furthermore, MLP performance with 92.44% accuracy using the Adam optimizer. The proposed CNN model achieves the lowest Loss value and compilation time. In addition, the models' recall value, precision, and f1-score are computed to pick out the most effective model for diagnosing breast cancer on numeric data.

Purpose To compare two deep learning-based commercially available artificial intelligence (AI) systems for mammography with digital breast tomosynthesis (DBT) and benchmark them against the performance of radiologists. Materials and Methods This retrospective study included consecutive asymptomatic patients who underwent mammography with DBT (2019-2020). Two AI systems (Transpara 1.7.0 and ProFound AI 3.0) were used to evaluate the DBT examinations. The systems were compared using receiver operating characteristic (ROC) analysis to calculate the area under the ROC curve (AUC) for detecting malignancy overall and within subgroups based on mammographic breast density. Breast Imaging Reporting and Data System results obtained from standard-of-care human double-reading were compared against AI results with use of the DeLong test. Results Of 419 female patients (median age, 60 years [IQR, 52-70 years]) included, 58 had histologically proven breast cancer. The AUC was 0.86 (95% CI: 0.85, 0.91), 0.93 (95% CI: 0.90, 0.95), and 0.98 (95% CI: 0.96, 0.99) for Transpara, ProFound AI, and human double-reading, respectively. For Transpara, a rule-out criterion of score 7 or lower yielded 100% (95% CI: 94.2, 100.0) sensitivity and 60.9% (95% CI: 55.7, 66.0) specificity. The rule-in criterion of higher than score 9 yielded 96.6% sensitivity (95% CI: 88.1, 99.6) and 78.1% specificity (95% CI: 73.8, 82.5). For ProFound AI, a rule-out criterion of lower than score 51 yielded 100% sensitivity (95% CI: 93.8, 100) and 67.0% specificity (95% CI: 62.2, 72.1). The rule-in criterion of higher than score 69 yielded 93.1% (95% CI: 83.3, 98.1) sensitivity and 82.0% (95% CI: 77.9, 86.1) specificity. Conclusion Both AI systems showed high performance in breast cancer detection but lower performance compared with human double-reading. Keywords: Mammography, Breast, Oncology, Artificial Intelligence, Deep Learning, Digital Breast Tomosynthesis © RSNA, 2024.

Background Earlier detection of breast cancer through mammography screening has reduced disease-specific mortality; however, confounding issues such as technical challenges, breast density, and tumor size can result in false negatives and ultimately later stage diagnosis. Next generation liquid biopsy has the potential to complement mammography and enable earlier detection for more women. We have previously demonstrated high sensitivity and specificity for early detection of invasive breast cancer (IBC) by utilizing a novel category of cancer-associated small RNAs, termed orphan noncoding RNAs (oncRNAs), through a liquid biopsy platform. Here, we further improve the ability to detect breast cancer in a larger, multi-source cohort through an AI-driven approach and demonstrate potential for detection of ductal carcinoma in-situ (DCIS). Methods We utilized The Cancer Genome Atlas (TCGA) small RNA-seq database to discover a library of 20,538 oncRNAs, through a female-specific analysis, that were significantly enriched among 1,103 breast tumors compared to 349 normal tissue samples spanning multiple tissue sites. The diagnostic performance of these oncRNAs were assessed in an independent cohort of archived serum samples from 663 female individuals, sourced from Indivumed (Hamburg, Germany), Proteogenex (Inglewood, CA), and MT Group (Los Angeles, CA), including 279 breast cancer patients of various stages (221 IBC and 58 DCIS; mean age: 57.0 ± 13.8 years; ever-smoker: 25.8%) and 304 age-matched controls (mean age: 58.5 ± 13.9 years; ever-smoker: 23.4%) without breast cancer. All samples were collected between 2010–2022 at time of diagnosis for breast cancer patients. We sequenced the small RNA content of these samples at an average depth of 25.28 ± 9.37 million 50-bp single-end reads. We detected 18,025 (87.8%) unique breast cancer-specific oncRNA species within at least one sample from the study cohort. We then trained a generative AI model using 5-fold cross-validation to predict cancer status for all samples. Results Our oncRNA-based model achieved an overall AUC of 0.95 (95% CI, 0.93–0.97) for prediction of IBC versus cancer-free controls with a sensitivity of 0.87 (0.82–0.91) at 90% specificity. We observed high sensitivities, also at 90% specificity, across all tumor stages and tumor sizes (Table 1). Sensitivities for the earliest stage and smallest tumor size were 0.87 (0.78–0.93) and 0.81 (0.61–0.93) for Stage I (n=83) and T1a–b ( >1mm to ≤10mm; n=26), respectively. Additionally, in a small single-source cohort, we also saw high model accuracy and sensitivity for DCIS, which we aim to confirm in additional cohorts. While our overall cancer cohort primarily consisted of individuals with luminal breast cancer, our model had high sensitivities across all breast cancer subtypes at 0.90 (0.84–0.94), 0.73 (0.59–0.85), and 0.86 (0.42–1.0) for luminal (n=181), HER2 positive (n=49), and triple negative (n=7), respectively. Conclusions We further demonstrate the potential utility of oncRNAs as a blood-based biomarker using an AI algorithm for sensitive and accurate early detection of breast cancer in a large cohort. Additionally, we have shown that this oncRNA-based assay performs well in detecting small, early-stage invasive breast tumors, with potential to detect precursors of breast cancer. Table 1: Model sensitivity in breast cancer by tumor stage and size For each tumor stage and size, as defined by the AJCC 7th Edition breast cancer staging system, sensitivity and 95% Pearson-Clopper confidence intervals (CI) are reported at 90% specificity for the number of samples (N). Citation Format: Noura Tbeileh, Taylor Cavazos, Mehran Karimzadeh, Jeffrey Wang, Alice Huang, Dung Ngoc Lam, Seda Kilinc, Jieyang Wang, Xuan Zhao, Andy Pohl, Helen Li, Lisa Fish, Kimberly Chau, Marra Francis, Lee Schwartzberg, Patrick Arensdorf, Hani Goodarzi, Fereydoun Hormozdiari, Babak Alipanahi. Cell-free orphan noncoding RNAs and AI enable early detection of invasive breast cancer and ductal carcinoma in-situ [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-13-08.

Bone cancer is a significant health issue that leads to severe complications and deaths worldwide. Early detection can significantly improve patient outcomes, often resulting in a complete cure. Traditional approaches to managing bone cancer can be augmented with technology-driven methods, particularly those enabled by artificial intelligence. Convolutional Neural Networks (CNNs) and their variants have proven effective in medical data analysis. However, deep learning techniques require enhancements to improve performance in cancer detection across various medical imaging modalities. In this paper, we propose a deep-learning framework that utilizes advanced CNN models for the automatic screening of bone cancer. We have enhanced both the CNN model and the ResNet-50 model, which are integral components of the proposed framework. Additionally, we introduced an algorithm called Learning-based Bone Cancer Detection (LbBCD), designed to optimize the utilization of these enhanced deep learning models to improve bone cancer detection efficiency. Our research emphasizes a Region of Interest (ROI) based approach to enhance the screening process for bone cancer. Using a benchmark dataset known as the Bone CT Scan dataset, our empirical study demonstrated that the proposed deep learning framework, integrated with enhanced CNN and ResNet-50 models, achieved remarkable performance. Specifically, the enhanced CNN model reached an accuracy of 90.90%, while the enhanced ResNet-50 model achieved an accuracy of 92.20%, outperforming state-of-the-art deep learning models. Therefore, this proposed system can be integrated into healthcare applications for automatically screening bone cancer, contributing to a clinical decision support system for healthcare professionals.

One of the fatal diseases that occurs in women is breast cancer and is associated with late diagnosis and poor access to medical care according to the patient's needs, therefore neural networks play a relevant role in detection of breast cancer and aims to be a support to guarantee its accuracy and reliability in cancer results. Therefore, the aim of the present systematic review is to learn how neural networks help to improve accuracy in breast cancer diagnosis through image recognition. For this, the formula generated with the PICO methodology was used; Likewise, the first result was 203 investigations related to the topic and based on the established inclusion and exclusion criteria, 20 final free access scientific articles were selected from the Scopus database. In relation to the results, it was found that the use of neural networks in the diagnosis of breast cancer, especially convolutional neural networks (CNN), has proven to be a promising tool to improve the accuracy and early detection of the disease, reaching achieve an accuracy of 98 % in the recognition of clinical images, which means a big difference compared to traditional methods. On the other hand, although there are challenges such as the limited availability of high-quality data sets and bias in training data, it is suggested to investigate the development of methods that integrate multiple sources of information and the use of deep learning techniques.

Breast cancer (BC) is the most common cause of women’s deaths worldwide. The mammography technique is the most important modality for the detection of BC. To detect abnormalities in mammographic images, the Breast Imaging Reporting and Data System (BI-RADs) is used as a baseline. The correct allocation of BI-RADs categories for mammographic images is always an interesting task, even for specialists. In this work, to detect and classify the mammogram images in BI-RADs, a novel hybrid model is presented using a convolutional neural network (CNN) with the integration of a support vector machine (SVM). The dataset used in this research was collected from different hospitals in the Qassim health cluster of Saudi Arabia. The collection of all categories of BI-RADs is one of the major contributions of this paper. Another significant contribution is the development of a hybrid approach through the integration of CNN and SVM. The proposed hybrid approach uses three CNN models to obtain ensemble CNN model results. This ensemble model saves the values to integrate them with SVM. The proposed system achieved a classification accuracy, sensitivity, specificity, precision, and F1-score of 93.6%, 94.8%, 96.9%, 96.6%, and 95.7%, respectively. The proposed model achieved better performance compared to previously available methods.

Detection of air traffic conflicts in a weather constrained airspace is challenging given the inherent uncertainties and aircraft maneuvers which give rise to new conflict birth-points. Traditional conflict detection tools are untenable in such situations as they primarily rely on flight-plan, aircraft performance characteristics and trajectories projection in short-term (2-4 minutes). This work adopts a convolutional neural network (CNN) model, on radar-like images, for conflict detection task in a constrained airspace. The CNN models are well-known for their learning capabilities when dealing with unstructured data like pixelated images. In this study, historical ADS-B data with weather constrained airspace is input as pixelated images to the CNN model. The learned model was compared with two well-known models for conflict detection (CD). The results demonstrated that the CNN based model was able to predict off-nominal conflict with high accuracy. The CNN model also demonstrated its ability to predict off-nominal conflict early for a given ten-minute look-ahead window. The CNN based model also showed low levels of false alarm signals as compared to other models. Generally speaking, all models showed low probabilities of miss-detection, mostly in the early phase of the 10-minute look-ahead window. This novel approach may serve to develop effective CD algorithms with longer look-ahead time and may aid in early detection of air traffic conflicts in non-nominal scenarios.

Background: Accurate assessment of a woman’s individualized breast cancer risk is necessary to inform shared decision-making regarding screening and risk-reducing strategies. Recently, deep learning techniques, including convolutional neural networks (CNN), have shown better predictive potential for breast cancer risk compared to mammographic density (MD). We evaluated whether combining clinical factors in the Breast Cancer Surveillance Consortium (BCSC) model, including MD, with a novel CNN-based mammographic evaluation more accurately predicts breast cancer risk than the BCSC model alone in a cohort of racially/ethnically diverse women. Methods: We conducted a retrospective cohort study of 23,552 women, age 35-74 years, who underwent screening mammography from 2014 to 2018 at Columbia University Irving Medical Center in New York City. We extracted data from the electronic health record (EHR) on breast cancer risk factors (age, race/ethnicity, prior benign breast biopsy, first degree family history of breast cancer, and MD). From this cohort, we identified 206 women who developed breast cancer by linkage to the tumor registry. We calculated 5-year invasive breast cancer risk using the BCSC model. We applied CNN-based breast cancer risk model to full-field craniocaudal mammographic views of both breasts, with an output of a risk score (range, 0-1). We used logistic regression models with breast cancer status as the outcome and predictors including clinical factors only (BCSC model) or combined with CNN risk score (hybrid model). We compared the prediction performance of these models via area under the receiver operating characteristics curves (AUCs) based on the DeLong test. We also calculated each model’s AUC for subgroups of age and race/ethnicity. Results: Among 23,552 evaluable women, mean age was 55.9 years (standard deviation [SD], 9.5) with 27% non-Hispanic White, 9% non-Hispanic Black, 36% Hispanic, 5% Asian, and 23% Other/Unknown race/ethnicity. Four percent had a first-degree family history of breast cancer, 10% had a prior benign breast biopsy, 45% had heterogeneously or extremely dense breasts on mammography, and 22% met high-risk criteria based upon a 5-year invasive breast cancer risk 1.67% according to the BCSC model. Mean CNN risk score was higher among breast cancer cases compared to unaffected controls (0.477 vs. 0.466, p=0.077). We found that the hybrid model outperformed the BCSC model (AUC of 0.676 vs. 0.640, respectively; p=0.003). In subgroup analyses, the hybrid model more accurately predicted breast cancer risk compared to the BCSC model among women age<50 (AUC of 0.713 vs. 0.645, respectively; p=0.078) and age>=50 (AUC of 0.663 vs. 0.625, respectively; p=0.026); non-Hispanic Black women (AUC of 0.794 vs. 0.663, respectively; p=0.028) and Hispanic women (AUC of 0.666 vs. 0.621, respectively; p=0.060). Conclusion: Among women undergoing screening mammography, a hybrid model incorporating a CNN-based mammography evaluation with clinical factors from the BCSC model more accurately predicted breast cancer risk relative to the BCSC model alone, particularly among racial and ethnic minorities. Combined with clinical risk factors, our CNN model may be used to efficiently predict breast cancer risk and inform risk-stratified breast cancer screening and prevention strategies. Comparing prediction performance for breast cancer risk of the BCSC model vs. hybrid modelBCSC ModelHybrid ModelP-value*AUC95% CIAUC95% CIAll patients0.6400.602-0.6830.6760.640-0.7110.003Age (years)<500.6450.562-0.7280.7130.648-0.7780.078>=500.6250.579-0.6710.6630.623-0.7030.026Race/ethnicityNon-Hispanic White0.6880.623-0.7540.7040.643-0.7660.230Non-Hispanic Black0.6630.539-0.7880.7940.704-0.8830.028Hispanic0.6210.560-0.6860.6660.597-0.7330.060Asian0.6160.440-0.7910.6510.461-0.8410.694 Citation Format: Alissa Michel, Vicky Ro, Julia E McGuinness, Simukayi Mutasa, Richard Ha, Katherine D Crew. Improving breast cancer risk prediction using a convolutional neural network-based mammographic evaluation in combination with clinical risk factors [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P2-10-03.

Breast Cancer has become one of the very crucial disease, increasing the mortality rate among woman at an early age worldwide. Therefore, early detection and timely treatment have become increasingly important in the field of medical science. Artificial Intelligence is now widely used for developing models which supports doctors in early detection of breast cancer. In this study, deep learning models are used for classification and prediction of breast cancer. For implementation image data are taken from Mendeley Database. Initially, a Convolutional Neural Network(CNN) is used to classify the images into Cancer and non-cancer with an accuracy of 84.94%. To increase the performance, the EfficientNetB0 model from the EfficientNet family is used. The performance of EfficientNetB0 is much better than the CNN model with the accuracy increased to 93%. However, the key limitation of EfficientNetB0 is that some of the cancer images are misclassified as non-cancer, which will be addressed in future study.

Understanding the learning mechanism of convolutional neural networks in spectral analysis

Breast cancer is an important topic in medical image analysis because it is a high-risk disease and the leading cause of death in women. Early detection of breast cancer improves treatment outcomes, which can be achieved by identifying it using mammography images. Computer-aided diagnostic systems detect and classify medical images of breast lesions, allowing radiologists to make accurate diagnoses. Deep learning algorithms improved the performance of these diagnoses systems. We utilized efficient deep learning approaches to propose a system that can detect breast cancer in mammograms. The proposed approach adopted relies on two main elements: improving image contrast to enhance marginal information and extracting discriminatory features sufficient to improve overall classification quality, these improvements achieved based on a new model from scratch to focus on enhancing the accuracy and reliability of breast cancer detection. The model trained on the digital database for screening mammography (DDSM) dataset and compared with different convolutional neural network (CNN) models, namely EfficientNetB1, EfficientNetB5, ResNet-50, and ResNet101. Moreover, to enhance the feature selection process, we have integrated adam optimizer in our methodology. In evaluation, the proposed method achieved 96.5% accuracy across the dataset. These results show the effectiveness of this method in identifying breast cancer through images.

In medical imaging, the effective detection and classification of Breast Cancer (BC) is a current research important task because of the still existing difficulty to distinguish abnormalities from normal breast tissues due to their subtle appearance and ambiguous margins and distinguish abnormalities from the normal breast. Moreover, BC detection based on an automated detection model is needed, because manual diagnosis faces problems due to cost and shortage of skilled manpower, and also takes a very long time. Using deep learning and ensemble feature selection techniques, in this paper, a novel framework is introduced for classifying BC from histopathology images. The five primary steps of the suggested framework are as follows: 1) to make the largest original dataset and then deep learning model with data augmentation to improve the learning. 2) The best features are selected by an Ensemble Filter Feature selection Method (EFFM) which combines the best feature subsets to produce the final feature subsets. 3) Then the pruned Convolution Neural Network (CNN) model is utilized to extract the optimal features. 4) Finally, the classification is done through the Triplet Attention based Efficient Network (TAENet) classifier. The suggested model produces a 98% accuracy rate after being trained and tested on two different histopathology imaging datasets including images from four different data cohorts. Subsequently, the suggested strategy outperforms the conventional ones since the ensemble filter habitually acquires the best features, and experimental results demonstrate the importance of the proposed approach

Among the many deep learning methods, the convolutional neural network (CNN) model has an excellent performance in image recognition. Research on identifying and classifying image datasets using CNN is ongoing. Animal species recognition and classification with CNN is expected to be helpful for various applications. However, sophisticated feature recognition is essential to classify quasi-species with similar features, such as the quasi-species of parrots that have a high color similarity. The purpose of this study is to develop a vision-based mobile application to classify endangered parrot species using an advanced CNN model based on transfer learning (some parrots have quite similar colors and shapes). We acquired the images in two ways: collecting them directly from the Seoul Grand Park Zoo and crawling them using the Google search. Subsequently, we have built advanced CNN models with transfer learning and trained them using the data. Next, we converted one of the fully trained models into a file for execution on mobile devices and created the Android package files. The accuracy was measured for each of the eight CNN models. The overall accuracy for the camera of the mobile device was 94.125%. For certain species, the accuracy of recognition was 100%, with the required time of only 455 ms. Our approach helps to recognize the species in real time using the camera of the mobile device. Applications will be helpful for the prevention of smuggling of endangered species in the customs clearance area.

Breast cancer is a common cause of female mortality in developing countries. Early detection and treatment are crucial for successful outcomes. Breast cancer develops from breast cells and is considered a leading cause of death in women. This disease is classified into two subtypes: invasive ductal carcinoma (IDC) and ductal carcinoma in situ (DCIS). The advancements in artificial intelligence (AI) and machine learning (ML) techniques have made it possible to develop more accurate and reliable models for diagnosing and treating this disease. From the literature, it is evident that the incorporation of MRI and convolutional neural networks (CNNs) is helpful in breast cancer detection and prevention. In addition, the detection strategies have shown promise in identifying cancerous cells. The CNN Improvements for Breast Cancer Classification (CNNI-BCC) model helps doctors spot breast cancer using a trained deep learning neural network system to categorize breast cancer subtypes. However, they require significant computing power for imaging methods and preprocessing. Therefore, in this research, we proposed an efficient deep learning model that is capable of recognizing breast cancer in computerized mammograms of varying densities. Our research relied on three distinct modules for feature selection: the removal of low-variance features, univariate feature selection, and recursive feature elimination. The craniocaudally and medial-lateral views of mammograms are incorporated. We tested it with a large dataset of 3002 merged pictures gathered from 1501 individuals who had digital mammography performed between February 2007 and May 2015. In this paper, we applied six different categorization models for the diagnosis of breast cancer, including the random forest (RF), decision tree (DT), k-nearest neighbors (KNN), logistic regression (LR), support vector classifier (SVC), and linear support vector classifier (linear SVC). The simulation results prove that our proposed model is highly efficient, as it requires less computational power and is highly accurate.