IECE Journal of Image Analysis and Processing

Open Access | Research Article | 23 May 2025

IRV2-hardswish Framework: A Deep Learning Approach for Deepfakes Detection and Classification

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Farooq Akhtar

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Rabbia Mahum

IECE Journal of Image Analysis and Processing | Volume 1, Issue 2: 45-56, 2025 | DOI: 10.62762/JIAP.2025.421251

Abstract

Deep learning models are pivotal in the advancements of Artificial Intelligence (AI) due to rapid learning and decision-making across various fields such as healthcare, finance, and technology. However, a harmful utilization of deep learning models poses a threat to public welfare, national security, and confidentiality. One such example is Deepfakes, which creates and modifies audiovisual data that humans cannot tell apart from the real ones. Due to the progression of deep learning models that produce manipulated data, accurately detecting and classifying deepfake data becomes a challenge. This paper presents a groundbreaking IRV2-Hardswish Framework for deepfake detection, leveraging a hyb... More >

Graphical Abstract

IRV2-hardswish Framework: A Deep Learning Approach for Deepfakes Detection and Classification

Open Access | Research Article | 20 March 2025

Plant Disease Detection Using Deep Learning Techniques

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Zunaira Zainab

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Rabbia Mahum

IECE Journal of Image Analysis and Processing | Volume 1, Issue 1: 36-44, 2025 | DOI: 10.62762/JIAP.2025.227089

Abstract

Plant diseases create one of the most serious risks to the world's food supply, reducing agricultural production and endangering millions of people's lives. These illnesses can destroy crops, disrupt food supply networks, and increase the danger of food deficiency, emphasizing the importance of establishing strong methods to protect the world's food sources. The approaches of deep learning have transformed the field of plant disease diagnosis, providing sophisticated and perfect solutions for early detection and management. However, a prevalent concern with deep learning models is their susceptibility to a lack of generalization and robustness when faced with novel crop and disease categorie... More >

Graphical Abstract

Plant Disease Detection Using Deep Learning Techniques

Open Access | Research Article | 14 March 2025

High-Quality Multi-Focus Image Fusion: A Comparative Analysis of DCT-Based Approaches with Their Variants

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IECE Journal of Image Analysis and Processing | Volume 1, Issue 1: 27-35, 2025 | DOI: 10.62762/JIAP.2024.764051

Abstract

Image fusion, especially in the context of multi-focus image fusion, plays a crucial role in digital image processing by enhancing the clarity and detail of visual content through the combination of multiple source images. Traditional spatial domain methods often suffer from issues like spectral distortion and low contrast, which has led researchers to explore techniques in the frequency domain, such as the Discrete Cosine Transform (DCT). DCT-based methods are particularly valued for their computational efficiency, making them a strong alternative, especially in applications like image compression and fusion. This study focuses on DCT-based approaches, including variants that incorporate Si... More >

Graphical Abstract

High-Quality Multi-Focus Image Fusion: A Comparative Analysis of DCT-Based Approaches with Their Variants

Open Access | Research Article | 16 February 2025

Leveraging Machine Learning and Deep Learning for Advanced Malaria Detection Through Blood Cell Images

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IECE Journal of Image Analysis and Processing | Volume 1, Issue 1: 17-26, 2025 | DOI: 10.62762/JIAP.2025.514726

Abstract

Malaria remains a significant global health challenge, causing hundreds of thousands of deaths annually, particularly in tropical and subtropical regions. This study proposes an advanced automated approach for malaria detection by classifying red blood cell images using machine learning and deep learning techniques. Three distinct models: Logistic Regression (LR), Support Vector Machine (SVM), and Inception-V3 were implemented and rigorously evaluated on a dataset comprising 27,558 cell images. The LR model achieved an accuracy of 65.38%, while SVM demonstrated improved classification performance with an accuracy of 84%. The deep learning-based Inception-V3 model outperformed both, achieving... More >

Graphical Abstract

Leveraging Machine Learning and Deep Learning for Advanced Malaria Detection Through Blood Cell Images

Open Access | Research Article | 08 December 2024 | Cited: 1

AlexNet based Ensembel Approach for Synthetic Aperture Radar Target Classification under Different Conditions

by

Noor Rahman

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Muzammil Khan

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Imran Khan

IECE Journal of Image Analysis and Processing | Volume 1, Issue 1: 5-16, 2024 | DOI: 10.62762/JIAP.2024.927304

Abstract

This paper presents an ensemble approach for Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) that integrates AlexNet, Support Vector Machine (SVM), and template matching through majority voting to improve classification accuracy under various operating conditions. The study utilizes the MSTAR dataset, focusing on both Standard Operating Conditions (SOC) and Extended Operating Conditions (EOC). The methodology begins with SAR image preprocessing, applying threshold segmentation with histogram equalization and morphological filtering to extract target regions. These regions undergo feature extraction, with AlexNet and SVM separately classifying the targets, while template mat... More >

Graphical Abstract

AlexNet based Ensembel Approach for Synthetic Aperture Radar Target Classification under Different Conditions

Open Access | Editorial | 04 October 2024

Navigating the Publication Process: What Editors of Journal of Image Analysis and Processing Expect

by

Muzammil Khan

IECE Journal of Image Analysis and Processing | Volume 1, Issue 1: 1-4, 2024 | DOI: 10.62762/JIAP.2024.674931

Abstract

This editorial provides a comprehensive guide for authors looking to publish in the Journal of Image Analysis and Processing. It outlines the key aspects that editors prioritize, including alignment with the journal’s aims and scope, originality, and technical rigor. Authors are encouraged to focus on innovative contributions in image processing, ensuring their research is well-structured, clearly written, and ethically sound. The article also emphasizes the importance of practical relevance, data transparency, and adhering to submission guidelines. By understanding these requirements, authors can improve their chances of successfully navigating the fast review process and achieving public... More >

IECE Journal of Image Analysis and Processing

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