اراﺋﻪ ﯾﮏ ﺳﯿﺴﺘﻢ ﺗﺸﺨﯿﺺ ﮐﺎﻣﭙﯿﻮﺗﺮی ﻫﻮﺷﻤﻨﺪ ﺟﺪﯾﺪ ﺑﺮای ﺗﺸﺨﯿﺺ ﺳﺮﻃﺎن ﺳﯿﻨﻪ ﺑﺎ اﺳﺘﻔﺎده از ﺗﺼﺎوﯾﺮ ﺣﺮارﺗﯽ

ﺳﺮﻃﺎن ﺳﯿﻨﻪ از ﺷﺎﯾﻊﺗﺮﯾﻦ دﻻﯾﻞ ﻣﺮگوﻣﯿﺮ در ﻣﯿﺎن زﻧﺎن ﺟﻬﺎن ﻣﯽﺑﺎ ﺷﺪ، اﻣﺎ ﺗ ﺸﺨﯿﺺ زودﻫﻨﮕﺎم و دﻗﯿﻖ اﯾﻦ ﻧﻮع ﺳﺮﻃﺎن ﻣﯽﺗﻮاﻧﺪ درﻣﺎن را ﺑﻪ ﻃﺮز ﭼﺸﻤﮕﯿﺮی ﺑﻬﺒﻮد ﺑﺨﺸﺪ. ﺗﺼﻮﯾﺮﺑﺮداری ﺣﺮارﺗﯽ ﯾﮑﯽ از روشﻫﺎی اوﻟﯿﻪ ﺗﺸﺨﯿﺺ ﺳﺮﻃﺎن ﺳﯿﻨﻪ اﺳﺖ. ﻫﻤﭽﻨﯿﻦ از ﺳﯿﺴﺘﻢ ﺗﺸﺨﯿﺺ ﮐﺎﻣﭙﯿﻮﺗﺮی ﻣﯽﺗﻮان ﺑﺮای ﮐﻤﮏ ﺑﻪ ﭘﺰﺷﮑﺎن ﺑﺮای اﻓﺰاﯾﺶ دﻗﺖ ﺗﻔﺴﯿﺮ ﻧﺘﺎﯾﺞ اﺳﺘﻔﺎده ﮐﺮد. در اﯾﻦ ﻣﻘﺎﻟﻪ، ﯾﮏ ﺳﯿﺴﺘﻢ ﺗﺸﺨﯿﺺ ﮐﺎﻣﭙﯿﻮﺗﺮی ﻫﻮ ﺷﻤﻨﺪ ﺑﺮای ﺗﺸﺨﯿﺺ ﺳﺮﻃﺎن ﺳﯿﻨﻪ ﺑﺎ ا ﺳﺘﻔﺎده از ﺗ ﺼﺎوﯾﺮ ﺣﺮارﺗﯽ اراﺋﻪ ﺷﺪه ا ﺳﺖ. ﺳﯿﺴﺘﻢ ﻫﻮ ﺷﻤﻨﺪ ﺗﺸﺨﯿﺺ ﮐﺎﻣﭙﯿﻮﺗﺮی اراﺋﻪ ﺷﺪه ﺷﺎﻣﻞ روش sfta ﺑﺮای ا ﺳﺘﺨﺮاج وﯾﮋﮔﯽ و اﻟﮕﻮرﯾﺘﻢ knn ،svm و d-tree ﺑﺮای ﻃﺒﻘﻪﺑﻨﺪی ﻧﺘﺎﯾﺞ ﻣﯽﺑﺎ ﺷﺪ. ﻋﻤﻠﮑﺮد ﺳﯿ ﺴﺘﻢ ﺗ ﺸﺨﯿﺺ ﮐﺎﻣﭙﯿﻮﺗﺮی ﻫﻮ ﺷﻤ ﻨﺪ ارا ﺋﻪ ﺷﺪه ﺑﺎ ا ﺳﺘ ﻔﺎده از ﭘﺎﯾ ﮕﺎه داده dmr-ir و ﭘﺎﯾ ﮕﺎه داده داﻧ ﺸ ﮕﺎه fluminense federal و matlab2018 ﺑﺮای ا ﺳﺘﻔﺎده از اﻟﮕﻮرﯾﺘﻢ اﻧﺘﺨﺎب وﯾﮋﮔﯽ ﻓﺎﺧﺘﻪ و ﺑﺪون اﻟﮕﻮرﯾﺘﻢ اﻧﺘﺨﺎب وﯾﮋﮔﯽ ارزﯾﺎﺑﯽ ﺷﺪها ﺳﺖ. ﻧﺘﺎﯾﺞ ﻧ ﺸﺎن ﻣﯽدﻫﺪ ﮐﻪ ﺑﻬﺘﺮﯾﻦ ﻣﯿﺎﻧﮕﯿﻦ ﺻﺤﺖ، ﺣ ﺴﺎ ﺳﯿﺖ و اﺧﺘ ﺼﺎ ﺻﯿﺖ ﺑﻪ ﺗﺮﺗﯿﺐ 99/5 ،99 و 98/03 در ﺻﺪ ﻣﯽﺑﺎ ﺷﺪ ﮐﻪ ﺑﺎﺑﮑﺎرﮔﯿﺮی اﻟﮕﻮرﯾﺘﻢ اﻧﺘﺨﺎب وﯾﮋﮔﯽ ﻓﺎﺧﺘﻪ و اﻟﮕﻮرﯾﺘﻢ ﻃﺒﻘﻪﺑﻨﺪی ﮐﻨﻨﺪه svm ﺑﺪﺳﺖ آﻣﺪهاﺳﺖ. ﻫﻤﭽﻨﯿﻦ ﺳﯿﺴﺘﻢ ﺗﺸﺨﯿﺺ ﮐﺎﻣﭙﯿﻮﺗﺮی اراﺋﻪ ﺷﺪه دارای ﻣﺰاﯾﺎﯾﯽ ﻧﺴﺒﺖ ﺑﻪ ﺳﺎﯾﺮ ﺳﯿﺴﺘﻢﻫﺎی ﺗﺸﺨﯿﺺ ﮐﺎﻣﭙﯿﻮﺗﺮی ﻣﯽﺑﺎﺷﺪ. اﯾﻦ ﻧﺘﺎﯾﺞ ﻧﺸﺎن ﻣﯽدﻫﺪ ﮐﻪ اﺳﺘﻔﺎده از روش اﺳﺘﺨﺮاج وﯾﮋﮔﯽ sfta، اﻟﮕﻮرﯾﺘﻢ اﻧﺘﺨﺎب وﯾﮋﮔﯽ ﻓﺎﺧﺘﻪ، اﻟﮕﻮرﯾﺘﻢ ﻃﺒﻘﻪﺑﻨﺪی svm و دادهﻫﺎی ﭘﺎﯾﮕﺎه داده dmr-ir در ﺳﯿ ﺴﺘﻢ ﺗ ﺸﺨﯿﺺ ﮐﺎﻣﭙﯿﻮﺗﺮی ﭘﯿﺸﻨﻬﺎدی، ﻣﯽﺗﻮاﻧﺪ ﺑﺎﻋﺚ ﺑﻬﺒﻮد ﻧﺘﺎﯾﺞ ارزﯾﺎﺑﯽ ﺷﻮد.

a new computer-aided diagnosis system for breast cancer detection in thermal images

breast cancer is one of the most common causes of death among women around the world, but early and accurate diagnosis of this type of cancer can dramatically improve treatment. thermal imaging is one of the primary methods of diagnosing breast cancer. the computer diagnosis system can also be used to help physicians to increase the accuracy of interpretation of results. this paper presents an intelligent computer diagnostic system for the detection of breast cancer using thermal imaging. the proposed intelligent computer diagnosis system includes sfta method for feature extraction and svm, knn and d-tree algorithms for classification of results. the performance of the proposed intelligent computer diagnosis system is evaluated using the dmr-ir and fluminense federal university databases and matlab2018, when using the cuckoo feature selection algorithm and without using the feature selection algorithm. the results show that the average accuracy, sensitivity and specificity are 99%, 99.5% and 98.03%, respectively, using the cuckoo feature selection algorithm and svm classification algorithm. also, the presented computer diagnostic system has advantages compared to other computer diagnosis systems. these results indicate that the use of sfta feature extraction method, cuckoo feature selection algorithm, svm classification algorithm and dmr-ir database in the proposed computer diagnosis system can improve the evaluation results.