João Otávio Bandeira Diniz1, Pedro Henrique Bandeira Diniz2, Thales Levi Azevedo Valente3, Aristófanes Corrêa Silva4, Anselmo Cardoso de Paiva5, Marcelo Gattass6. 1. Applied Computing Group, Federal University of Maranhão - UFMA, NCA, Av. dos Portugueses, SN, Bacanga, São Luís, MA 65085-580, Brazil. Electronic address: joao.obd@gmail.com. 2. Pontifical Catholic University of Rio de Janeiro - PUC - Rio, R. São Vicente, 225, Gávea, Rio de Janeiro, RJ, 22453-900, Brazil. Electronic address: pedro_hbd@hotmail.com. 3. Pontifical Catholic University of Rio de Janeiro - PUC - Rio, R. São Vicente, 225, Gávea, Rio de Janeiro, RJ, 22453-900, Brazil. Electronic address: selaht7@gmail.com. 4. Applied Computing Group, Federal University of Maranhão - UFMA, NCA, Av. dos Portugueses, SN, Bacanga, São Luís, MA 65085-580, Brazil. Electronic address: aricsilva@gmail.com. 5. Applied Computing Group, Federal University of Maranhão - UFMA, NCA, Av. dos Portugueses, SN, Bacanga, São Luís, MA 65085-580, Brazil. Electronic address: anselmo.c.paiva@gmail.com. 6. Pontifical Catholic University of Rio de Janeiro - PUC - Rio, R. São Vicente, 225, Gávea, Rio de Janeiro, RJ, 22453-900, Brazil. Electronic address: mgattass@tecgraf.puc-rio.br.
Abstract
BACKGROUND AND OBJECTIVE: The processing of medical image is an important tool to assist in minimizing the degree of uncertainty of the specialist, while providing specialists with an additional source of detect and diagnosis information. Breast cancer is the most common type of cancer that affects the female population around the world. It is also the most deadly type of cancer among women. It is the second most common type of cancer among all others. The most common examination to diagnose breast cancer early is mammography. In the last decades, computational techniques have been developed with the purpose of automatically detecting structures that maybe associated with tumors in mammography examination. This work presents a computational methodology to automatically detection of mass regions in mammography by using a convolutional neural network. METHODS: The materials used in this work is the DDSM database. The method proposed consists of two phases: training phase and test phase. The training phase has 2 main steps: (1) create a model to classify breast tissue into dense and non-dense (2) create a model to classify regions of breast into mass and non-mass. The test phase has 7 step: (1) preprocessing; (2) registration; (3) segmentation; (4) first reduction of false positives; (5) preprocessing of regions segmented; (6) density tissue classification (7) second reduction of false positives where regions will be classified into mass and non-mass. RESULTS: The proposed method achieved 95.6% of accuracy in classify non-dense breasts tissue and 97,72% accuracy in classify dense breasts. To detect regions of mass in non-dense breast, the method achieved a sensitivity value of 91.5%, and specificity value of 90.7%, with 91% accuracy. To detect regions in dense breasts, our method achieved 90.4% of sensitivity and 96.4% of specificity, with accuracy of 94.8%. CONCLUSIONS: According to the results achieved by CNN, we demonstrate the feasibility of using convolutional neural networks on medical image processing techniques for classification of breast tissue and mass detection.
BACKGROUND AND OBJECTIVE: The processing of medical image is an important tool to assist in minimizing the degree of uncertainty of the specialist, while providing specialists with an additional source of detect and diagnosis information. Breast cancer is the most common type of cancer that affects the female population around the world. It is also the most deadly type of cancer among women. It is the second most common type of cancer among all others. The most common examination to diagnose breast cancer early is mammography. In the last decades, computational techniques have been developed with the purpose of automatically detecting structures that maybe associated with tumors in mammography examination. This work presents a computational methodology to automatically detection of mass regions in mammography by using a convolutional neural network. METHODS: The materials used in this work is the DDSM database. The method proposed consists of two phases: training phase and test phase. The training phase has 2 main steps: (1) create a model to classify breast tissue into dense and non-dense (2) create a model to classify regions of breast into mass and non-mass. The test phase has 7 step: (1) preprocessing; (2) registration; (3) segmentation; (4) first reduction of false positives; (5) preprocessing of regions segmented; (6) density tissue classification (7) second reduction of false positives where regions will be classified into mass and non-mass. RESULTS: The proposed method achieved 95.6% of accuracy in classify non-dense breasts tissue and 97,72% accuracy in classify dense breasts. To detect regions of mass in non-dense breast, the method achieved a sensitivity value of 91.5%, and specificity value of 90.7%, with 91% accuracy. To detect regions in dense breasts, our method achieved 90.4% of sensitivity and 96.4% of specificity, with accuracy of 94.8%. CONCLUSIONS: According to the results achieved by CNN, we demonstrate the feasibility of using convolutional neural networks on medical image processing techniques for classification of breast tissue and mass detection.
Authors: Felipe André Zeiser; Cristiano André da Costa; Tiago Zonta; Nuno M C Marques; Adriana Vial Roehe; Marcelo Moreno; Rodrigo da Rosa Righi Journal: J Digit Imaging Date: 2020-08 Impact factor: 4.056
Authors: João O B Diniz; Darlan B P Quintanilha; Antonino C Santos Neto; Giovanni L F da Silva; Jonnison L Ferreira; Stelmo M B Netto; José D L Araújo; Luana B Da Cruz; Thamila F B Silva; Caio M da S Martins; Marcos M Ferreira; Venicius G Rego; José M C Boaro; Carolina L S Cipriano; Aristófanes C Silva; Anselmo C de Paiva; Geraldo Braz Junior; João D S de Almeida; Rodolfo A Nunes; Roberto Mogami; M Gattass Journal: Multimed Tools Appl Date: 2021-06-24 Impact factor: 2.757