000			02120nam a22002657a 4500
999			_c83246 _d83246
003			20221105101943.0
005			20250505111037.0
008			221105b ||||| |||| 00| 0 eng d
041			_aeng
082			_a616.240285
100	1		_aOraño, Jannie Fleur V. _eauthor
245			_aCXR-based lung disease classification using convolutional neural network / _cJannie Fleur V. Oraño
300			_aviii, 83 leaves: _bcolor illustrations; _c28 cm
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
502			_aThesis (College of Computer Studies) -- Cebu Institute of Technology University _dOctober 2019
504			_aIncludes bibliographical references.
520	3		_aLung disease like effusion, pneumothorax, atelectasis and tuberculosis are some of the most severe and prevailing health problems in people’s life. A large percentage of the human population around the world is diagnosed annually with lung disease affecting adults, teens, children, smokers, and even non-smokers making it be considered as the leading cause of death and disability worldwide. Early and accurate diagnosis of chest diseases in mandatory and needed for timely and successful treatment, prevents further complications and a higher likelihood of survival. This study demonstrated the feasibility of classifying lung diseases in chest X-rays using conventional and deep learning approaches. With 8,125 sample images, the neural network model managed to achieve 82.53% accuracy in the classification. This accuracy rate indicates the generated model can significantly automate the differential diagnosis of lung disease using chest radiograph. The developed applications, when used as a second opinion, can help assists radiologist and doctors in their diagnosis and decision making. However, this accuracy can be further improved if more and balanced training images will be utilized.
650		0	_aLungs _xDiseases.
650		0	_aNeural networks (Computer science)
650		2	_aDiagnostic Imaging _xmethods
655		0	_aAcademic theses.
942			_2ddc _cT&D