thermogram Breast Cancer Detection : a comparative study of two machine learning techniques

Breast cancer is considered one of the major threats for women’s health all over the world. The World Health Organization (WHO) has reported that 1 in every 12 women could be subject to a breast abnormality during her lifetime. To increase survival rates, it is found that it is very effective to early detect breast cancer. Mammography-based breast cancer screening is the leading technology to achieve this aim. However, it still can not deal with patients with dense breast nor with tumor size less than 2 mm. Thermography-based breast cancer approach can address these problems. In this paper, a thermogram-based breast cancer detection approach is proposed. This approach consists of four phases: (1) Image Pre-processing using homomorphic filtering, top-hat transform and adaptive histogram equalization, (2) ROI Segmentation using binary masking and K-mean clustering, (3) feature extraction using signature boundary, and (4) classification in which two classifiers, Extreme Learning Machine (ELM) and Multilayer Perceptron (MLP), were used and compared. The proposed approach is evaluated using the public dataset, DMR-IR. Various experiment scenarios (e.g., integration between geometrical feature extraction, and textural features extraction) were designed and evaluated using different measurements (i.e., accuracy, sensitivity, and specificity). The results showed that ELM-based results were better than MLP-based ones with more than 19%

An Efficient CBIR System for Medical Images Using Neural Network

This paper introduces an innovative Content-Based Image Retrieval (CBIR) system that has been specifically developed for medical databases. Its objective is to resolve the drawbacks of conventional keyword-based search approaches when considering the widespread digitization of medical illustrations, diagrams, and paintings. In contrast to conventional methods that rely on textual queries, CBIR systems effectively locate and retrieve relevant images by analyzing image content using computer vision and image processing techniques, as well as information retrieval and database methods.A key challenge in CBIR lies in bridging the semantic gap between high-level user queries, often expressed through example images, and the low-level features of images such as texture, shape, and objects. This paper explores techniques to mitigate this disparity, enhancing the system's ability to accurately interpret user queries and retrieve relevant images. The proposed CBIR system operates within a medical database containing images of various human organs, including the brain, heart, hand, chest, spine, and shoulder, categorized into six distinct classes. By leveraging low-level image features such as texture and shape, extracted using methods like mean, variance, standard deviation, area, perimeter, circularity, and aspect ratio analysis, the system performs iterative searches to retrieve relevant images.Classification of retrieved images is accomplished using Artificial Neural Networks (ANN), which have demonstrated efficacy in medical image classification tasks based on imaging modalities and the presence of normal or abnormal conditions. Performance evaluation of the CBIR system is conducted using confusion matrices to calculate precision and recall, essential metrics for assessing retrieval accuracy. By focusing on medical datasets and integrating advanced feature extraction and classification techniques, this CBIR system aims to significantly enhance image retrieval efficiency and accuracy, particularly in the context of medical applications where precise retrieval of relevant images is critical for diagnostic and research purposes. &nbsp

A new approach for breast abnormality detection based on thermography

Breast cancer is one of the most common women cancers in the world. In this paper, a new approach based on thermography for the early detection of breast abnormality is proposed. The study involved 80 breast thermograms collected from the PROENG public database which consists of 50 healthy breasts and 30 with some findings. Image processing techniques such as segmentation, texture analysis and mathematical morphology were used to train a support vector machine (SVM) classifier for automatic detection of breast abnormality. After conducting several tests, we obtained very interesting and motivating results. Indeed, our method  showed a high performance in terms of sensitivity of 93.3%, a specificity of 90% and an accuracy of 91.25%. The final results let us conclude that infrared thermography with the help of an adequate automatic classification algorithm can be a valuable and reliable complementary tool for radiologist in detecting breast cancer and thereby helping to reduce mortality rates

GENETIC PROGRAMMING TO OPTIMIZE PERFORMANCE OF MACHINE LEARNING ALGORITHMS ON UNBALANCED DATA SET

Data collected from the real world is often imbalanced, meaning that the distribution of data across known classes is biased or skewed. When using machine learning classification models on such imbalanced data, predictive performance tends to be lower because these models are designed with the assumption of balanced classes or a relatively equal number of instances for each class. To address this issue, we employ data preprocessing techniques such as SMOTE (Synthetic Minority Oversampling Technique) for oversampling data and random undersampling for undersampling data on unbalanced datasets. Once the dataset is balanced, genetic programming is utilized for feature selection to enhance performance and efficiency. For this experiment, we consider an imbalanced bank marketing dataset from the UCI Machine Learning Repository. To assess the effectiveness of the technique, it is implemented on four different classification algorithms: Decision Tree, Logistic Regression, KNN (K-Nearest Neighbors), and SVM (Support Vector Machines). Various metrics including accuracy, balanced accuracy, recall, F-score, ROC (Receiver Operating Characteristics) curve, and PR (Precision-Recall) curve are compared for unbalanced data, oversampled data, undersampled data, and cleaned data with Tomek-Links for each algorithm. The results indicate that all four algorithms perform better when oversampling the minority class to half of the majority class and undersampling the majority class examples to match the minority class, followed by performing Tomek-Links on the balanced dataset

Termografia como meio de diagnóstico complementar da mamografia

O cancro de mama representa a segunda causa de morte por cancro, logo após o cancro de pulmão. Atualmente a sua deteção precoce é efetuada por mamografia. Porém, esta fornece apenas informação anatómica detetando o cancro quando este já se encontra estabelecido. Nas lesões, antes da sua formação, ocorrem alterações fisiológicas que poderão ser detetadas por termografia, uma vez que a angiogénese criada pelo tumor e o aumento das suas necessidades metabólicas induzem um aumento de temperatura. A termografia foi introduzida como meio de diagnóstico de cancro de mama em 1965 e inicialmente foi bem aceite. Porém, alguns estudos colocaram em causa a eficiência deste método quando comparado com a mamografia, pelo que a comunidade médica foi perdendo o interesse. Recentemente houve vários avanços a nível das câmaras térmicas, pelo que são necessários novos estudos para verificar o potencial destas no rastreio de cancro de mama. O objetivo deste trabalho é efetuar um estudo comparativo entre as mamografias e os termogramas mamários, de modo a fornecer novos dados sobre a aplicação da termografia no complemento da mamografia na deteção e monitorização do cancro de mama. Para tal, foram adquiridos termogramas a 151 utentes (após consentimento informado) que participavam no Programa de Rastreio de Cancro de Mama da Liga Portuguesa Contra o Cancro – Núcleo Regional do Norte, onde o rastreio é efetuado por mamografia. Para a recolha das imagens foi utilizada a câmara térmica FLIR T-365 que se encontrava posicionada a cerca de 1 metro de distância da área mamária, numa sala com uma temperatura entre 18ºC e 23ºC e onde potenciais fontes de calor foram eliminadas para reduzir os artefactos térmicos. A análise da mamografia foi efetuada por médicos Radiologistas e classificadas segundo a Sistema de Classificação de Marselha das European Guidelines; nos termogramas foi analisada a assimetria das mamas e calculadas as diferenças de temperatura, classificando-as depois numa escala pré-estabelecida; por último foi efetuada a comparação dos resultados. Chegou-se à conclusão que a termografia pode ajudar na deteção de lesões mamárias, contudo com a escala apresentada não foi possível estabelecer uma relação direta entre o aumento da temperatura na termografia e a malignidade da lesão.After the lung cancer, breast cancer represents the second most important cause of death. Currently, the early detection is achieved by means of mammography providing anatomical details, although the cancer is only detected when it is already present. Before that, several physical alterations occur that can be detected by thermography, due to the increase of temperature created by the tumor angiogenesis and the higher metabolic rate. In 1965, the thermography was introduced as a diagnosis tool for breast cancer and initially very well accepted; however lost interest by the community due to the less efficiency of this technique when compared with mammography were reported in some studies. Recently new improvements were achieved in thermography cameras, thus new studies are needed to observe the potencial of this technique in breast cancer screening. The aim of this work is to perform a comparative study between mammography and breast thermograms, to give new insight concerning the application of the thermography ad a complementary tool of mammography in the early detection of breast cancer. For this study, 151 patients performed the screening mammography program of the Liga Portuguesa Contra o Cancro- Núcleo Regional do Norte, and in addition, have made a thermographic examination (after informed consent). This exam was performed using a thermographic camera FLIR T- 365, at 1 meter of distance from the breast, in a room with a controlled temperature in between 18ºC and 23ªC, and no potential sources of additional heat in order to reduce thermal artifacts. Radiologists analyzed each mammographic image using Marseille System by European Guidelines. The analysis of the thermograms was base on asymmetry and skin temperature differences. A rating scale was proposed and the results were further compared with the Mammography scores. As main conclusion, the thermography can help in early detection of breast lesions, however the proposal rating scale don’t allows to assess the relationship between the temperature increase in thermography and the malignancy of the lesions