Fused Deep Features Based Classification Framework for COVID-19 Classification with Optimized MLP

COVID-19 adi verilen yeni tip Koronavirus hastaligi oldukca hizli yayilmaya devam etmektedir. Bazi spesifik semptomlar gosterse de hemen her bireyde farkli semptomlar gosterebilen bu hastalik yuzbinlerce hastanin hayatini kaybetmesine neden olmustur. Saglik uzmanlari, daha fazla yasam kaybini onlemek icin cok calissalar da, hastalik yayilma orani cok yuksektir. Bu nedenle Bilgisayar Destekli Teshis (BDT) ve Yapay Zeka (YZ) algoritmalarinin destegi hayati onem tasimaktadir. Bu calismada, belirtilen COVID-19 algilama ihtiyaclarini karsilamak icin gunumuzun en etkili goruntu analiz yontemi olan Evrisimli Sinir Agi (ESA) mimarisinin optimizasyonuna dayali bir yontem onerilmistir. Ilk olarak, COVID-19 goruntuleri ResNet-50 ve VGG-16 mimarileri kullanilarak egitilir. Ardindan, bu iki mimarinin son katmanindaki ozellikler fuzyon islemi uygulanmistir. Fuzyon islemi ile elde edilen bu yeni goruntu ozellikleri matrisleri, COVID-19 tespiti icin siniflandirilir. Siniflandirma islemi icin Balina Optimizasyon Algoritmasi (BOA) ile optimize edilmis Cok Katmanli Bir Algilayici (CKA) yapisi kullanilir. Elde edilen sonuclar, onerilen cercevenin performansinin VGG-16 performansindan neredeyse % 4,5 ve ResNet-50 performansindan neredeyse % 3,5 daha yuksek oldugunu gostermektedir.

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