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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Sohel, Amir
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Computer Science & IT Electrical & Electronics Engineering

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Automated tomato leaf disease recognition using deep convolutional networks Sohel, Amir; Rahman, Md Mizanur; Hasan, Md Umaid; Islam, MD Kafiul; Rukhsara, Lamia; Rabeya, Tapasy
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1850-1860

Abstract

Agriculture is essential for the entire global population. An advanced, robust, and empirically sound agriculture sector is essential for nourishing the global population. Various leaf diseases cause financial hardships for farmers and related businesses. Early identification of foliar diseases in crops would greatly help farmers, leading to a substantial increase in agricultural productivity. The tomato is a widely recognized and nourishing food that is easily accessible and highly favored by farmers. Early diagnosis of tomato leaf diseases is crucial to maximize tomato crop production. This study aims to utilize a deep learning approach to accurately detect and classify damaged leaves and disease patterns in tomato leaf images. By employing a substantial quantity of deep convolutional network models, we achieved a high level of precision in diagnosing the condition. The dataset used in our study work is a self-contained dataset obtained by direct observation of tomato fields in rural areas of Bangladesh. It consists of four classes: healthy, black mold, grey mold, and powdery mildew. In this study work, we utilized various image pre-processing techniques and applied VGG16, InceptionV3, DenseNet121, and AlexNet models. Our results showed that the DenseNet121 model attained the higher accuracy of 97%. This discovery guarantees accurate detection of tomato diseases in a rapid manner, ushering in a new agricultural revolution.
Efficient brain cancer identification using ResNet50 and ResNet50 V2: a comparative study with a primary MRI dataset Mizanur Rahman, Md; Jahan, Israt; Das, Rana; Tasmia Alvi, Syada; Abida Anjum Era, Chowdhury; Asif Khan Akash, Atik; Sohel, Amir; Zaman, Zahura
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9142

Abstract

Primary malignant brain tumors along with central nervous system cause a significant amount of deaths every year, making brain cancer a major worldwide health problem. In South Asian countries, the number of patients suffering from brain cancer is steadily rising. Treatment effectiveness and improved patient outcomes depend on early detection. Using a dataset consisting of 6056 original raw MRI scans, this study evaluates how well convolutional neural networks (CNNs) diagnose brain cancer. We present ResNet50 and ResNet50V2 models assessed for their effectiveness in identifying brain cancers. Transfer learning and fine-tuning were employed to enhance model performance. The models demonstrated strong performance, with 87-99% accuracy rate. ResNet50V2 achieved the highest 99% accuracy. To detect tumor early, this work emphasizes how well the CNN-based machine learning methods help as timely intercession and patient care is necessary. Early prediction with 100% confidence and reliable precision is a critical issue in the modern world. Our goal is to use advanced algorithms to forecast images affected by cancer. Lastly, we will deploy an automated system that will enable us to confidently identify images affected by cancer. Our suggested methodology and its application could significantly impact the field of medical science by combining computer vision and health informatics.
Co-Authors Abida Anjum Era, Chowdhury Asif Khan Akash, Atik Das, Rana Hasan, Md Umaid Islam, Md Kafiul Jahan, Israt Mizanur Rahman, Md Rabeya, Tapasy Rahman, Md Mizanur Rukhsara, Lamia Tasmia Alvi, Syada Zaman, Zahura