An efficient end-to-end deep neural network for interstitial lung disease
recognition and classification

JENY, Afsana Ahsan; JUNAYED, Masum Shah; ISLAM, Md Baharul; SHAH, AFM Shahen; AHMED, Ikhtiar

doi:10.55730/1300-0632.3846

An efficient end-to-end deep neural network for interstitial lung disease recognition and classification

Masum Shah Junayed, (Bahçeşehir Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü, İstanbul, Türkiye)

Afsana Ahsan Jeny, (Bahçeşehir Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü, İstanbul, Türkiye)

Md Baharul Islam, (Bahçeşehir Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü, İstanbul, Türkiye)

Ikhtiar Ahmed, (Department of Data Science, TU Dortmund University, Dortmund, Germany)

AFM Shahen Shah (Yıldız Teknik Üniversitesi, Elektrik Elektronik Fakültesi, Elektronik ve Haberleşme Mühendisliği Bölümü, İstanbul, Türkiye)

Turkish Journal of Electrical Engineering and Computer Sciences

17 0

Yıl: 2022 Cilt: 30 Sayı: 4 Sayfa Aralığı: 1235 - 1250 Metin Dili: İngilizce DOI: 10.55730/1300-0632.3846 İndeks Tarihi: 18-07-2022

An efficient end-to-end deep neural network for interstitial lung disease recognition and classification

Öz:

The automated Interstitial Lung Diseases (ILDs) classification technique is essential for assisting clinicians during the diagnosis process. Detecting and classifying ILDs patterns is a challenging problem. This paper introduces an end-to-end deep convolution neural network (CNN) for classifying ILDs patterns. The proposed model comprises four convolutional layers with different kernel sizes and Rectified Linear Unit (ReLU) activation function, followed by batch normalization and max-pooling with a size equal to the final feature map size well as four dense layers. We used the ADAM optimizer to minimize categorical cross-entropy. A dataset consisting of 21328 image patches of 128 CT scans with five classes is taken to train and assess the proposed model. A comparison study showed that the presented model outperformed pre-trained CNNs and five-fold cross-validation on the same dataset. For ILDs pattern classification, the proposed approach achieved the accuracy scores of 99.09% and the average F score of 97.9% that outperforms three pre-trained CNNs. These outcomes show that the proposed model is relatively state-of-the-art in precision, recall, f score, and accuracy.

Anahtar Kelime:

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık

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APA	JUNAYED M, JENY A, ISLAM M, AHMED I, SHAH A (2022). An efficient end-to-end deep neural network for interstitial lung disease recognition and classification. , 1235 - 1250. 10.55730/1300-0632.3846
Chicago	JUNAYED Masum Shah,JENY Afsana Ahsan,ISLAM Md Baharul,AHMED Ikhtiar,SHAH AFM Shahen An efficient end-to-end deep neural network for interstitial lung disease recognition and classification. (2022): 1235 - 1250. 10.55730/1300-0632.3846
MLA	JUNAYED Masum Shah,JENY Afsana Ahsan,ISLAM Md Baharul,AHMED Ikhtiar,SHAH AFM Shahen An efficient end-to-end deep neural network for interstitial lung disease recognition and classification. , 2022, ss.1235 - 1250. 10.55730/1300-0632.3846
AMA	JUNAYED M,JENY A,ISLAM M,AHMED I,SHAH A An efficient end-to-end deep neural network for interstitial lung disease recognition and classification. . 2022; 1235 - 1250. 10.55730/1300-0632.3846
Vancouver	JUNAYED M,JENY A,ISLAM M,AHMED I,SHAH A An efficient end-to-end deep neural network for interstitial lung disease recognition and classification. . 2022; 1235 - 1250. 10.55730/1300-0632.3846
IEEE	JUNAYED M,JENY A,ISLAM M,AHMED I,SHAH A "An efficient end-to-end deep neural network for interstitial lung disease recognition and classification." , ss.1235 - 1250, 2022. 10.55730/1300-0632.3846
ISNAD	JUNAYED, Masum Shah vd. "An efficient end-to-end deep neural network for interstitial lung disease recognition and classification". (2022), 1235-1250. https://doi.org/10.55730/1300-0632.3846

APA	JUNAYED M, JENY A, ISLAM M, AHMED I, SHAH A (2022). An efficient end-to-end deep neural network for interstitial lung disease recognition and classification. Turkish Journal of Electrical Engineering and Computer Sciences, 30(4), 1235 - 1250. 10.55730/1300-0632.3846
Chicago	JUNAYED Masum Shah,JENY Afsana Ahsan,ISLAM Md Baharul,AHMED Ikhtiar,SHAH AFM Shahen An efficient end-to-end deep neural network for interstitial lung disease recognition and classification. Turkish Journal of Electrical Engineering and Computer Sciences 30, no.4 (2022): 1235 - 1250. 10.55730/1300-0632.3846
MLA	JUNAYED Masum Shah,JENY Afsana Ahsan,ISLAM Md Baharul,AHMED Ikhtiar,SHAH AFM Shahen An efficient end-to-end deep neural network for interstitial lung disease recognition and classification. Turkish Journal of Electrical Engineering and Computer Sciences, vol.30, no.4, 2022, ss.1235 - 1250. 10.55730/1300-0632.3846
AMA	JUNAYED M,JENY A,ISLAM M,AHMED I,SHAH A An efficient end-to-end deep neural network for interstitial lung disease recognition and classification. Turkish Journal of Electrical Engineering and Computer Sciences. 2022; 30(4): 1235 - 1250. 10.55730/1300-0632.3846
Vancouver	JUNAYED M,JENY A,ISLAM M,AHMED I,SHAH A An efficient end-to-end deep neural network for interstitial lung disease recognition and classification. Turkish Journal of Electrical Engineering and Computer Sciences. 2022; 30(4): 1235 - 1250. 10.55730/1300-0632.3846
IEEE	JUNAYED M,JENY A,ISLAM M,AHMED I,SHAH A "An efficient end-to-end deep neural network for interstitial lung disease recognition and classification." Turkish Journal of Electrical Engineering and Computer Sciences, 30, ss.1235 - 1250, 2022. 10.55730/1300-0632.3846
ISNAD	JUNAYED, Masum Shah vd. "An efficient end-to-end deep neural network for interstitial lung disease recognition and classification". Turkish Journal of Electrical Engineering and Computer Sciences 30/4 (2022), 1235-1250. https://doi.org/10.55730/1300-0632.3846