Analysis of Mammographic Images for Early Detection of Breast Cancer Using Machine Learning Techniques

Breast cancer is the main reason for death among women. Radiographic images obtained from mammography equipment are one of the most frequently used techniques for helping in early detection of breast cancer. The motivation behind this study is to focus the tumour types of breast cancer images .It is methodology to anticipated a sickness in view of the visual conclusion of breast disease tumour types with precision, particularly when numerous feature are related. Breast Cancer (BC) is one such sample where the phenomenon is very complex furthermore numerous feature of tumour types are included. In the present investigation, various pattern recognition techniques were used for the classification of breast cancer using mammograms image processing techniques .The pattern recognition techniques for tumour image enhancements, segmentation, texture based image feature extraction and subsequent classification of breast cancer mammogram image was successfully performed. When two machine learning techniques such as Artificial Neural Network (ANN), Support Vector Machine (SVM) were used to classify 120 images, it was observed from the results that Artificial Neural Network classifiers demonstrated the h classification rate 91.31% and the SVM with both Radial Basis Function (RBF) and linear kernel classifiers demonstrated the highest classification rate of 92.11% and RBF classification rate is 92.85%

Analysis of Mammographic Images for Early Detection of Breast Cancer Using Machine Learning Techniques

Breast cancer is the main reason for death among women. Radiographic images obtained from mammography equipment are one of the most frequently used techniques for helping in early detection of breast cancer. The motivation behind this study is to focus the tumour types of breast cancer images .It is methodology to anticipated a sickness in view of the visual conclusion of breast disease tumour types with precision, particularly when numerous feature are related. Breast Cancer (BC) is one such sample where the phenomenon is very complex furthermore numerous feature of tumour types are included. In the present investigation, various pattern recognition techniques were used for the classification of breast cancer using mammograms image processing techniques .The pattern recognition techniques for tumour image enhancements, segmentation, texture based image feature extraction and subsequent classification of breast cancer mammogram image was successfully performed. When two machine learning techniques such as Artificial Neural Network (ANN), Support Vector Machine (SVM) were used to classify 120 images, it was observed from the results that Artificial Neural Network classifiers demonstrated the h classification rate 91.31% and the SVM with both Radial Basis Function (RBF) and linear kernel classifiers demonstrated the highest classification rate of 92.11% and RBF classification rate is 92.85%

The Simultaneous Detection and Classification of Mass and Calcification Leading to Breast Cancer in Mammograms

In this paper, we present a novel method for detecting and classifying breast cancer calcification and masses in a single step. The detection and classification steps of calcifications and masses identifiable with a mammogram image are typically performed independently even though their simultaneous solution may lead to a more efficient approach. Our novel method utilizes a Convolutional Neural Network (CNN) to classify the calcifications and masses of different cropped images of a mammogram. We utilize a sliding window detector to break apart full mammogram images into sub-images, and identify and classify the observable objects in the sub-images. We receive multiple probabilities for each sub-image for the different possible classifications. We rank the sub-images, displaying the coordinates of the highest ranked sub-images for each classification. The results of this process are that we detect 46% of cancer within the mammograms and properly classify 64% of the calcifications and masses identified

Identification of Anomalies in Mammograms through Internet of Medical Things (IoMT) Diagnosis System

Breast cancer is the primary health issue that women may face at some point in their lifetime. This may lead to death in severe cases. A mammography procedure is used for finding suspicious masses in the breast. Teleradiology is employed for online treatment and diagnostics processes due to the unavailability and shortage of trained radiologists in backward and remote areas. The availability of online radiologists is uncertain due to inadequate network coverage in rural areas. In such circumstances, the Computer-Aided Diagnosis (CAD) framework is useful for identifying breast abnormalities without expert radiologists. This research presents a decision-making system based on IoMT (Internet of Medical Things) to identify breast anomalies. The proposed technique encompasses the region growing algorithm to segment tumor that extracts suspicious part. Then, texture and shape-based features are employed to characterize breast lesions. The extracted features include first and second-order statistics, center-symmetric local binary pattern (CS-LBP), a histogram of oriented gradients (HOG), and shape-based techniques used to obtain various features from the mammograms. Finally, a fusion of machine learning algorithms including K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA are employed to classify breast cancer using composite feature vectors. The experimental results exhibit the proposed framework's efficacy that separates the cancerous lesions from the benign ones using 10-fold cross-validations. The accuracy, sensitivity, and specificity attained are 96.3%, 94.1%, and 98.2%, respectively, through shape-based features from the MIAS database. Finally, this research contributes a model with the ability for earlier and improved accuracy of breast tumor detection

Evaluation of segmentation and detection in computer aided diagnostic methods on selected mammogram images

Uvod: Mamografija se je v zadnjih letih razširila kot primarna diagnostična preiskava za detekcijo bolezni dojk, predvsem kot metoda izbora presejalnih programov. Pri sami interpretaciji mamogramov, pravočasnem odkrivanju karcinomov in razlikovanju med benignimi in malignimi tumorskimi masami so v veliko pomoč orodja računalniško vodene diagnoze. Delijo se na Computer Aided Detection (CADe) in Computer Aided Diagnosis (CACx). Omenjena orodja se pri svojem delovanju poslužujejo metod strojnega učenja. Razdelitev slike v homogene regije tekstur je eden prvih korakov pri razumevanju, analizi in poglobljenem vpogledu v dano sliko. Med CAD metode med drugim spadajo tudi segmentacijski algoritmi pri obdelavi digitalnih slik. V nalogi smo osredotočeni na segmentacije z upragovljanjem, segmentacije z določanjem območij in segmentacije z učenjem. Namen: Namen diplomske naloge je ugotoviti, katera izmed izbranih oblik segmentacije v sklopu metod računalniško vodene detekcije najbolj ustrezno segmentira sliko pri izbrani bazi mamografskih slik. Metode dela: Pri pisanju diplomske naloge je bila uporabljena metoda deskripcije pri opisovanju pojmov in metoda kompilacije pri uporabi izpiskov, navedb in citatov drugih avtorjev. Nato je na podlagi knjižnih virov opravljen kvalitativni pregled gradiva z namenom primerjave načinov segmentacije in detekcije interesnih področij na mamografskih slikah izbrani bazi mamografskih slik. Rezultati: Primerjane so študije, ki testirajo svoje metode segmentacije in detekcije na javno dostopni bazi mamografskih slik Digital Database For Screening Mammography (DDSM). Opravljen je pregled rezultatov v obliki tabele in ovrednoteni so posledični izsledki. Komentirali smo prednosti in slabosti različnih metod in predlagali najučinkovitejšo. Razprava in zaključek: Zaključimo lahko, da metode, predlagane s strani pregledanih študij zadovoljivo interpretirajo sliko pri izbrani bazi mamografskih slik, a ne dosegajo enako konsistentnih rezultatov kot zdravniki specialisti. Na podlagi pregledanih raziskav lahko sklepamo, da višje rezultate dosegajo segmentacije, ki uporabljajo pri svojem delovanju strojno učenje in proces rojenja. Težavo pri doseganju ponovljivih in primerljivih rezultatov študij predstavlja uporaba različnih mamografskih slik za analizo in adaptacija nevronskih mrež s strani raziskovalcev. Preden se lahko CADe (angl. Computer Aided Detection) metode uvrstijo med komplementarne tehnike pri diagnostiki mamografskih slik v sami klinični praksi, je potreben nadaljnji razvoj področja in konsistentno doseganje zadovoljivih rezultatov, predvsem visokih vrednosti senzitivnosti, točnosti in AUC.Introduction: Mammography has become the number one detection method of breast cancer in the recent years, especially through various preliminary screening programs. Mammogram analysis through computer aided detection has been established as a big aid to radiologists in early cancer detection rates. Computer aided detection (CADe) represents a segment of Computer aided diagnosis (CAD), both of which employ the methods of machine learning in their workings. One of prerequisites for efficient detection of tumor masses is adequate segmentation of presented breast tissue. This work is focused mainly on threshhold based segmentation, region based segmentation and segmentation based on learning. Purpose: We intended to establish the efficiency of segmentation methods and positive detection rates used in modern computer aided detection processes. Methods: A descriptive method was used to explain the basic concepts of segmentation and detection of cancer tissue in CADe methods through extensive study of available material on current research of the field in question. The results were presented in a qualitative manner with a commentary on efficiency and viability of methods used. Results: Studies, that tested their segmentation and CADe methods on the publicly available database Digital Datbase for Screening Mammography (DDSM), were reviewed. We compared selected studies from the field of computer aided detection and assesed their efficiency in breast tissue segmentation and positive detection rates of cancer mass. Discussion and conclusion: It was concluded that CADe methods adequately segment and detect cancer tissue in mammograms, but do not yet reach the efficiency of trained radiologists. It is evident that methods employing machine learning algorithms and clustering segmentation tend to produce better overall results than the rest of reviewed methods. The studied sources suggest there is a lack of uniform, publicly accessible mammogram databases that could be used to further research the field with practically comparable results. As such, CADe methods and the segmentation processes involved show promise in the future of automatic interpretation of mammography screening

Performance of a CAD scheme applied to images obtained from mammographic film digitization and full-field digital mammography (FFDM).

This work has as purpose to compare the effects of a CAD scheme applied to digitized and \ud direct digital mamograms sets. A routine designed to be applied to mammogram in \ud DICOM standard was developed and a schema based on the Watershed Transform to \ud masses detection was applied to 252 ROIs from 130 digitized mammograms, resulting in \ud 92% of true positive and 10% of false positives. For clustered microcalcifications \ud detection, another procedure was applied to 165 ROIs from 120 mammograms, resulting in \ud 93% of true positive and 16% of false positive. By using the same procedures to 154 \ud digital mammograms obtained from FFDM, the rates have shown a little decrease in the \ud scheme performance: 89% of true positive and 16% of false positive for masses detection; \ud 90% of true positive and 27% of false positive for clusters detection. Although the tests \ud with digital mammograms have been carried with a smaller number of images and \ud different cases compared to the digitized ones, including several dense breasts images, the \ud results can be considered comparable, mainly forclustered microcalcifications detection \ud with a difference of only 3% between the sensibility rates for the both images sets. Another \ud important feature affecting these results is the contrast difference between the two images \ud set. This implies the need of extensive investigations not only with a larger number of \ud cases from FFDM but also on the parameters related to its image acquisition as well as to \ud its corresponding processing.Este trabalho tem como objetivo comparar os resultados de um esquema CAD aplicado em \ud conjunto de mamografias digitalizadas e em um conjunto de mamografias obtidas de um \ud mamógrafo digital. Para extrair as imagens do padrão DICOM, padrão utilizado pelos \ud mamógrafos digitais, uma rotina computacional foi desenvolvida. Para a detecção de \ud nódulos, um esquema baseado em Transforma Watershed foi aplicado a 252 regiões de \ud interesse (ROIs) de 130 mamografias digitalizadas, resultando em 92% de verdadeiro \ud positivo e 10%de falsos positivos. Para a detecção de microcalcificações agrupadas, outro \ud procedimento foi aplicado a165 ROIs extraídas de 120 mamografias digitalizadas, \ud resultando em 93% de verdadeiro positivo e 16% de falso positivo. Ao utilizar os mesmos \ud procedimentos para154 mamografias digitais obtidas a partir de um FFDM, as taxas \ud mostraram uma diminuição pequena no desempenho: 89% do verdadeiro positivo e 16% \ud de falso positivo para a detecção de nódulos, e 90% de verdadeiro positivo e 27% de falsos \ud positivo para a detecção de clusters de microcalcificações. Embora os testes com \ud mamografias digitais tenham sido realizados com um menor número de imagens e casos \ud diferentes em comparação com os digitalizados, incluindo várias imagens de mamas \ud densas, os resultados podem ser considerados comparáveis, principalmente para a detecção \ud de clusters de microcalcificações com uma diferença de apenas 3% entre as taxas de \ud sensibilidade para as imagens dos dois conjuntos. Outra característica importante que afeta \ud esses resultados é a diferença de contraste dos dois grupos de imagens analisados. Isto \ud implica na necessidade de extensas investigações não só com um maior número de casos \ud de mamografias digitais, mas também um estudo sobre os parâmetros relacionados a \ud aquisição da imagem, bem como para o seu processamentoCNPqFAPESPHospital of Clinics in Botucatu/S

DETECÇÃO E DIAGNÓSTICO DE MASSAS EM MAMOGRAFIA: revisão bibliográfica

Resumo: O câncer de mama tem se tornado cada dia mais freqüente entre a população feminina acima dos 40 anos. Somente para o ano de 2011 são estimados, no Brasil, 49 mil novos casos. Uma das maneiras para detectar os tumores não palpáveis que causam câncer de mama é realizar uma radiografia (mamografia) das mamas. A  mamografia é atualmente a melhor técnica de detecção precoce de lesões não apalpáveis na mama com altas chances de ser um câncer curável. Sabe-se que as chances de cura do câncer de mama são, relativamente altas, se detectado nos estágios inicias. Entretanto, a sensibilidade desse exame pode variar bastante, em decorrência de fatores como qualidade do exame ou experiência do especialista. Dessa forma, a utilização de sistemas CAD e CADx tem contribuído para aumentar as chances de uma detecção e diagnósticos corretos, ou seja, uma segunda opinião, auxiliando os especialistas na tomada de decisões em um tratamento do câncer de mama. Este artigo faz uma revisão bibliográfica de trabalhos voltados para detecção e diagnóstico de massas.Palavras-chave: Massa. Mamografia. Detecção. Diagnóstico. Câncer de mama.MAMMOGRAPHY MASS DETECTION AND DIAGNOSIS: a surveyAbstract: Breast cancer has become increasingly common among the female population over 40 years old. Only for the year 2011 are estimated, in Brazil, 49 000 new cases. One way to detect non-palpable tumors that cause breast cancer is to perform an X-ray (mammogram) of the breasts. Mammography is currently the best technique for early detection of non-palpable breast lesions with high chances of being a curable cancer. It is known that the chances of a cure for breast cancer are relatively high if detected in early stages. However, the sensitivity of this exam can vary greatly due to factors such as quality of examination or experience of the specialist. Thus, the use of CAD systems and CADX has contributed to increase the chances of detection and correct diagnosis, working as a second opinion in treatment of breast cancer. This article is a literature review of studies focused on detection and diagnosis of masses.Keywords: Mass. Mammography. Detection. Diagnosis. Breast cancer.DETECCIÓN Y DIAGNÓSTICO DE MASAS EN UNA MAMOGRAFÍA: una revisión de la literatura Resumen: El cáncer de mama se ha tornado cada vez más común entre la población femenina de más de 40 años. Sólo para el año 2011 se estima que en Brasil habrán 49 000 nuevos casos. Una forma de detectar tumores no palpables que causan el cáncer de mama es realizar una radiografía (mamografía) de los senos. La mamografía es actualmente la mejor técnica para la detección precoz de lesiones mamarias no palpables, con altas posibilidades de ser un cáncer curable. Se sabe que las posibilidades de una cura para el cáncer de mama son relativamente altas si se detecta en etapas tempranas. Sin embargo, la sensibilidad de esta prueba pueden variar considerablemente debido a factores como la calidad de los exámenes o la experiencia del especialista. Por lo tanto, el uso de sistemas CAD y CADX ha contribuido a aumentar las posibilidades de  detección y el diagnóstico correcto, o una segunda opinión, ayudando a los expertos en la tomada de decisiones en el tratamiento del cáncer de mama. Este artículo es una revisión de la literatura de trabajos sobre detección y diagnóstico de masas.Palabras clave: Masa. Mamografía. Detección. Diagnóstico de cáncer de mama