Breast cancer is one of the most frequent malignancies among women worldwide. Methods for screening and diagnosis allow health care professionals to provide personalized treatments that improve the outcome and survival. Scientists and physicians are working side-by-side to develop evidence-based guidelines and equipment to detect cancer earlier. However, the lack of comprehensive interdisciplinary information and understanding between biomedical, medical, and technology professionals makes innovation of new screening and diagnosis tools difficult. This critical review gathers, for the first time, information concerning normal breast and cancer biology, established and emerging methods for screening and diagnosis, staging and grading, molecular and genetic biomarkers. Our purpose is to address key interdisciplinary information about these methods for physicians and scientists. Only the multidisciplinary interaction and communication between scientists, health care professionals, technical experts and patients will lead to the development of better detection tools and methods for an improved screening and early diagnosis.

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Breast cancer, screening and diagnostic tools: All you need to know.

Breast cancer plays a significant role in affecting female mortality. Researchers are actively seeking to develop early detection methods of breast cancer. Several technologies contributed to the reduction in mortality rate from this disease, but early detection contributes most to preventing disease spread, breast amputation and death. Thermography is a promising technology for early diagnosis where thermal cameras employed are of high resolution and sensitivity. The combination of Artificial Intelligence (AI) with thermal images is an effective tool to detect early stage breast cancer and is foreseen to provide impressive predictability levels. This paper reviews systematically the related works employing thermography with AI highlighting their contributions and drawbacks and proposing open issues for research. Several different types of Artificial Neural Networks (ANNs) and deep learning models were used in the literature to process thermographic images of breast cancer, such as Radial Basis Function Network (RBFN), K-Nearest Neighbors (KNN), Probability Neural Network (PNN), Support Vector Machine (SVM), ResNet50, SeResNet50, V Net, Bayes Net, Convolutional Neural Networks (CNN), Convolutional and DeConvolutional Neural Networks (C-DCNN), VGG-16, Hybrid (ResNet-50 and V-Net), ResNet101, DenseNet and InceptionV3. Previous studies were found limited to varying the numbers of thermal images used mostly from DMR-IR database. In addition, analysis of the literature indicate that several factors do affect the performance of the Neural Network used, such as Database, optimization method, Network model and extracted features. However, due to small sample size used, most of the studies achieved a classification accuracy of 80% to 100%.

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A Systematic Review of Breast Cancer Detection Using Thermography and Neural Networks

Breast cancer is not preventable To reduce the death rate and improve the survival chances of breast cancer patients, early and accurate detection is the only panacea Delay in diagnosis of this disease causes 60% of deaths Thermal imaging is a low-risk modality for early breast cancer decision making without injecting any form of energy into the human body Thermography as a screening tool was first introduced and well accepted in 1956 However, a study in 1977 found that it lagged behind other screening tools and is subjective Soon after, its use was discontinued This review discusses various screening tools used to detect breast cancer with a focus on thermography along with their advantages and shortcomings With the maturation of thermography equipment and technological advances, this technique is emerging and has become the refocus of many biomedical researchers across the globe in the past decade This study dispenses an exhaustive review of the work done related to interpretation of breast thermal variations and confers the discipline, frameworks, and methodologies used by different authors to diagnose breast cancer Different performance metrics like accuracy, specificity, and sensitivity have also been examined This paper outlines the most pressing research gaps for future work to improvise the accuracy of results for diagnosis of breast abnormalities using image processing tools, mathematical modelling and artificial intelligence However, supplementary research is needed to affirm the potential of this technology for predicting breast cancer risk effectively Altogether, our findings inform that it is a promising research problem and a potential solution for early detection of breast cancer in younger women

Thermal Imaging - An Emerging Modality for Breast Cancer Detection: A Comprehensive Review

Breast cancer is one of the deadly cancer types that causes high mortality among women globally. Meanwhile, Deep Learning (DL) emerges as the most frequently utilized and rapidly developing branch of classical machine learning. The study examines a modern Computer-Aided Diagnosis (CAD) framework that uses DL to extract features and classify them for aiding radiologists in breast cancer diagnosis. This is accomplished through four distinct experimentations aimed at identifying the most optimal method of effective classification. Here, the first uses Deep CNNs that are pre-trained, such as AlexNet, GoogleNet, ResNet50, and Dense-Net121. The second is based on experiments using Deep CNNs to extract features and applying them onto a Support Vector Machine algorithm using three different kernels. The next one involves the fusion of different deep features for demonstrating the classification improvement by fusion of these deep features. The final experiment involves Principal Component Analysis (PCA) for reducing the computational cost and for decreasing the larger feature vectors created during fusion. The abovesaid experimentations are carried out in two different mammogram datasets namely MIAS and INbreast. The classification accuracy attained for both datasets through the fuzing of deep features (97.93% for MIAS and 96.646% for INbreast) is the highest compared with the state-of-the-art frameworks. In contrast, the classification performance did not enhance while applying the PCA on combined deep features; but the decrease in execution time provides a reduced computational cost.Abbreviations: CAD: Computer Aided Diagnosis; CNN: Convolution Neural Network; CSI: Classification Success Index; DCNN: Deep Convolution Neural Network; DICOM: Digital Imaging and Communications in Medicine; DL: Deep Learning; FC layer: Fully Connected layer; FFDM: Full-Field Digital Mammograms; FN: False Negative; FP: False Positive; ICSI: Individual Classification Success Index; MIAS: Mammographic Image Analysis Society; ML: Machine Learning; MLO: Medio-Lateral Oblique; PCA: Principal Component Analysis; PGM: Portable Gray Map; PPV: Positive Predictive Value; RBF: Radial Basis Function; SGDM: Stochastic Gradient Descent with Momentum; SVM: Support Vector Machine; TN: True Negative; TP: True Positive; TPR: True Positive Rate; UK: United Kingdom 

Multi-Deep CNN based Experimentations for Early Diagnosis of Breast Cancer

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The importance, status, and perspectives of hybrid lanthanide-doped upconversion nanothermometers for theranostics.

Thermal imaging is an emerging, low-risk modality for early breast cancer decision making without injecting any form of energy into the body. To reduce the burden on radiologists, use of automation is suggested to facilitate better interpretation of thermal images. Hotspots are segmented in the thermograms by k-means clustering algorithm. The features of left and right breasts are more correlated for normal cases and are less correlated for abnormal patients. The investigation results reveal that significant difference in feature values of left and right breast of an abnormal patient is depicted by first order statistical features, namely, kurtosis and variance.

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Detection of Breast Pathology using Thermography as a Screening Tool

Breast cancer is largely responsible for female mortality across globe. Infrared imaging helps to screen early abnormal signs based on the difference in contralateral temperatures of the breasts an...

Aayesha Hakim

Papers

Thermal Imaging - An Emerging Modality for Breast Cancer Detection: A Comprehensive Review

Detection of Breast Pathology using Thermography as a Screening Tool

Designing a Three-layer Back Propagation Artificial Neural Network for Breast Thermogram Classification