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Deep Learning-Based Lung Sound Classification Using Mel-Spectrogram Features for Early Detection of Respiratory Diseases Yabani, Midfai; Faisal, Mohammad Reza; Indriani, Fatma; Nugrahadi, Dodon Turianto; Kartini, Dwi; Satou, Kenji
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 1 (2026): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i1.1256

Abstract

Respiratory diseases such as asthma, chronic obstructive pulmonary disease, and pneumonia remain among the leading causes of death globally. Traditional diagnostic approaches, including auscultation, rely heavily on the subjective expertise of medical practitioners and the quality of the instruments used. Recent advancements in artificial intelligence offer promising alternatives for automated lung sound analysis. However, audio is an unstructured data format that must be converted into a suitable format for AI algorithms. Another significant challenge lies in the imbalanced class distribution within available datasets, which can adversely affect classification performance and model reliability. This study applied several comprehensive preprocessing techniques, including random undersampling to address data imbalance, resampling audio at 4000 Hz for standardization, and standardizing audio duration to 2.7 seconds for consistency. Feature extraction was then performed using the Mel Spectrogram method, converting audio signals into image representations to serve as input for classification algorithms based on deep learning architectures. To determine optimal performance characteristics, various Convolutional Neural Network (CNN) architectures were systematically evaluated, including LeNet-5, AlexNet, VGG-16, VGG-19, ResNet-50, and ResNet-152. VGG-16 achieved the highest classification accuracy of the tested models at 75.5%, demonstrating superior performance in respiratory sound classification tasks. This study demonstrates the potential of AI-based lung sound classification systems as a complementary diagnostic tool for healthcare professionals and the general public in supporting early identification of respiratory abnormalities and diseases. The findings suggest that automated lung sound analysis could enhance diagnostic accessibility and provide more valuable support for clinical decision-making in respiratory healthcare applications
Co-Authors Abadi, Friska Abdul Gafur Adi Mu'Ammar, Rifqi Adi, Puput Dani Prasetyo Adi, Puput Dani Prasetyo Ahmad Rusadi Ahmad Rusadi Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Aida, Nor Aji Triwerdaya Alfando, Muhammad Alvin Andi Farmadi Andi Farmadi Andi Farmadi Andi Farmadi Ando Hamonangan Saragih Apriana, Susi Ardiansyah Sukma Wijaya Arfan Eko Fahrudin Arifin Hidayat Azwari, Ayu Riana Sari Azwari, Ayu RianaSari Bachtiar, Adam Mukharil Badali, Rahmat Amin Bahriddin Abapihi Bedy Purnama Cahyadi, Rinova Firman Dike Bayu Magfira, Dike Bayu Djordi Hadibaya Dwi Kartini Dwi Kartini Dwi Kartini, Dwi Emy Iryanie, Emy Faisal Murtadho Faisal, Mohammad Reza Fajrin Azwary Fatma Indriani Fhadilla Muhammad Fitra Ahya Mubarok Fitria Agustina fitria Fitriani, Karlina Elreine Fitrinadi Friska Abadi Gunawan Gunawan Gunawan Gunawan Halim, Kevin Yudhaprawira Hariyady, Hariyady Herteno, Rudy Herteno, Rudy Heru Kartika Candra, Heru Kartika Huynh, Phuoc-Hai Ichsan Ridwan Indah Ayu Septriyaningrum Irwan Budiman Irwan Budiman Irwan Budiman Ismail Didit Samudro Julius Tunggono Jumadi Mabe Parenreng Junaidi, Ridha Fahmi Kartika, Najla Putri Keswani, Ryan Rhiveldi Kevin Yudhaprawira Halim Liling Triyasmono M Kevin Warendra M. Apriannur Martalisa, Asri Maulidha, Khusnul Rahmi Mera Kartika Delimayanti Miftahul Muhaemen Muhamad Ihsanul Qamil Muhammad Alkaff Muhammad Anshari Muhammad Haekal Muhammad Hasan Muhammad Irfan Saputra Muhammad Itqan Masdadi Muhammad Itqan Mazdadi Muhammad Janawi Muhammad Khairin Nahwan Muhammad Mirza Hafiz Yudianto Muhammad Nazar Gunawan Muhammad Reza Faisal, Muhammad Reza Muhammad Rofiq Muhammad Sholih Afif Muhammad Solih Afif Muliadi Muliadi Muliadi MULIADI -, MULIADI Muliadi Aziz Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi Muliadi, M Musyaffa, Muhammad Hafizh Nafis Satul Khasanah Nahdhatuzzahra Nahdhatuzzahra Ngo, Luu Duc Noor Hidayah Nursyifa Azizah Ori Minarto Padhilah, Muhammad Pirjatullah Pirjatullah Pirjatullah Prastya, Septyan Eka Priyatama, Muhammad Abdhi Radityo Adi Nugroho Rahayu, Fenny Winda Rahmad Ubaidillah Rahmat Ramadhani, Rahmat Ramadhan, Muhammad Rizky Aulia Riadi, Putri Agustina Rifki Izdihar Oktvian Abas Pullah Rifki Riza Susanto Banner Rizal, Muhammad Nur Rizki Amelia Rizki, M. Alfi Rozaq, Hasri Akbar Awal Rudy Herteno Rudy Herteno Saman Abdurrahman Saputro, Setyo Wahyu Saputro, Setyo Wahyu Saputro, Setyo Wahyu Saragih, Triando Hamonangan Satou, Kenji Selvia Indah Liany Abdie Setyo Wahyu Saputro sholih Afif Siti Napi'ah Soesanto, Oni Sri Cahyo Wahyono Sri Rahayu Sri Redjeki Sri Redjeki Totok Wianto Totok Wiyanto Tri Mulyani Triando Hamonangan Saragih Umar Ali Ahmad Utomo, Edy Setyo Wahyu Dwi Styadi Wahyu Saputro, Setyo Wardana, Muhammad Difha Winda Agustina Yabani, Midfai Yanche Kurniawan Mangalik YILDIZ, Oktay Yudha Sulistiyo Wibowo Zamzam, Yra Fatria