Marcin Woźniak1, Dawid Połap2, Giacomo Capizzi3, Grazia Lo Sciuto4, Leon Kośmider5, Katarzyna Frankiewicz6. 1. Institute of Mathematics, Silesian University of Technology, Kaszubska 23, Gliwice 44-100, Poland; Department of Electric, Electronic and Informatics Engineering, University of Catania, Viale A. Doria 6, Catania 95125, Italy. Electronic address: marcin.wozniak@polsl.pl. 2. Institute of Mathematics, Silesian University of Technology, Kaszubska 23, Gliwice 44-100, Poland; Department of Electric, Electronic and Informatics Engineering, University of Catania, Viale A. Doria 6, Catania 95125, Italy. Electronic address: dawid.polap@polsl.pl. 3. Institute of Mathematics, Silesian University of Technology, Kaszubska 23, Gliwice 44-100, Poland; Department of Electric, Electronic and Informatics Engineering, University of Catania, Viale A. Doria 6, Catania 95125, Italy. Electronic address: gcapizzi@diees.unict.it. 4. Department of Electric, Electronic and Informatics Engineering, University of Catania, Viale A. Doria 6, Catania 95125, Italy. Electronic address: glosciuto@dii.unict.it. 5. School of Pharmacy with the Division of Laboratory Medicine in Sosnowiec, Department of General and Analytical Chemistry Medical University of Silesia, Jagiellońska 4, Sosnowiec 41-200, Poland. Electronic address: leon.kosmider@sum.edu.pl. 6. Specialist Hospital Sz. Starkiewicz in Da̧browa Górnicza, Zagłȩbiowskie Oncology Centre, Szpitalna 13, Da̧browa Górnicza 41-300, Poland. Electronic address: frankiewicz90@gmail.com.
Abstract
BACKGROUND AND OBJECTIVE: In medical examinations doctors use various techniques in order to provide to the patients an accurate analysis of their actual state of health. One of the commonly used methodologies is the x-ray screening. This examination very often help to diagnose some diseases of chest organs. The most frequent cause of wrong diagnosis lie in the radiologist's difficulty in interpreting the presence of lungs carcinoma in chest X-ray. In such circumstances, an automated approach could be highly advantageous as it provides important help in medical diagnosis. METHODS: In this paper we propose a new classification method of the lung carcinomas. This method start with the localization and extraction of the lung nodules by computing, for each pixel of the original image, the local variance obtaining an output image (variance image) with the same size of the original image. In the variance image we find the local maxima and then by using the locations of these maxima in the original image we found the contours of the possible nodules in lung tissues. However after this segmentation stage we find many false nodules. Therefore to discriminate the true ones we use a probabilistic neural network as classifier. RESULTS: The performance of our approach is 92% of correct classifications, while the sensitivity is 95% and the specificity is 89.7%. The misclassification errors are due to the fact that network confuses false nodules with the true ones (6%) and true nodules with the false ones (2%). CONCLUSIONS: Several researchers have proposed automated algorithms to detect and classify pulmonary nodules but these methods fail to detect low-contrast nodules and have a high computational complexity, in contrast our method is relatively simple but at the same time provides good results and can detect low-contrast nodules. Furthermore, in this paper is presented a new algorithm for training the PNN neural networks that allows to obtain PNNs with many fewer neurons compared to the neural networks obtained by using the training algorithms present in the literature. So considerably lowering the computational burden of the trained network and at same time keeping the same performances.
BACKGROUND AND OBJECTIVE: In medical examinations doctors use various techniques in order to provide to the patients an accurate analysis of their actual state of health. One of the commonly used methodologies is the x-ray screening. This examination very often help to diagnose some diseases of chest organs. The most frequent cause of wrong diagnosis lie in the radiologist's difficulty in interpreting the presence of lungs carcinoma in chest X-ray. In such circumstances, an automated approach could be highly advantageous as it provides important help in medical diagnosis. METHODS: In this paper we propose a new classification method of the lung carcinomas. This method start with the localization and extraction of the lung nodules by computing, for each pixel of the original image, the local variance obtaining an output image (variance image) with the same size of the original image. In the variance image we find the local maxima and then by using the locations of these maxima in the original image we found the contours of the possible nodules in lung tissues. However after this segmentation stage we find many false nodules. Therefore to discriminate the true ones we use a probabilistic neural network as classifier. RESULTS: The performance of our approach is 92% of correct classifications, while the sensitivity is 95% and the specificity is 89.7%. The misclassification errors are due to the fact that network confuses false nodules with the true ones (6%) and true nodules with the false ones (2%). CONCLUSIONS: Several researchers have proposed automated algorithms to detect and classify pulmonary nodules but these methods fail to detect low-contrast nodules and have a high computational complexity, in contrast our method is relatively simple but at the same time provides good results and can detect low-contrast nodules. Furthermore, in this paper is presented a new algorithm for training the PNN neural networks that allows to obtain PNNs with many fewer neurons compared to the neural networks obtained by using the training algorithms present in the literature. So considerably lowering the computational burden of the trained network and at same time keeping the same performances.