Journal of Innovative Image Processing最新文献

Brain Tumor Classification using Convolutional Neural Network 利用卷积神经网络进行脑肿瘤分类

Journal of Innovative Image Processing Pub Date : 2024-03-01 DOI: 10.36548/jiip.2024.1.002

T.R. Ganesh Babu, V. Varsha, T. S. Yamuna Sri, S. P. Kumari

引用次数: 0

Hyperspectral Image Analysis using LSTM and 2D CNN and its Application in Remote Sensing 使用 LSTM 和二维 CNN 的高光谱图像分析及其在遥感中的应用

Journal of Innovative Image Processing Pub Date : 2023-12-01 DOI: 10.36548/jiip.2023.4.002

Amit Kumar Jha, Ram Krishna Maharjan, Nanda Bikram Adhikari

引用次数: 0

Revolutionizing Lung Cancer Diagnosis: A Comprehensive Review of Image Processing Techniques for Early Detection and Precision Medicine 肺癌诊断的革命：用于早期检测和精准医疗的图像处理技术综述

Journal of Innovative Image Processing Pub Date : 2023-12-01 DOI: 10.36548/jiip.2023.4.001

Sanjay S Tippannavar, Yashwanth S D, Gayatri S, Eshwari A Madappa

{"title":"Revolutionizing Lung Cancer Diagnosis: A Comprehensive Review of Image Processing Techniques for Early Detection and Precision Medicine","authors":"Sanjay S Tippannavar, Yashwanth S D, Gayatri S, Eshwari A Madappa","doi":"10.36548/jiip.2023.4.001","DOIUrl":"https://doi.org/10.36548/jiip.2023.4.001","url":null,"abstract":"According to World Health Organisation (WHO), lung cancer is the leading cause of cancer-related fatalities in both genders and has the highest fatality rate. Early detection of pulmonary nodules is essential to improving the significant survival rate of lung cancer due to the typical proliferation of lung cells. Studies on lung cancer indicate that smoking is the primary cause of this disease, which is more common in women nowadays and causes more deaths than breast cancer. Age, gender, race, socioeconomic status, exposure to the environment, air pollution, alcohol consumption, and second-hand smoking are a few more factors that could be significant in causing lung cancer. Early detection of lung cancer is achieved through a variety of image processing techniques, such as computed tomography (CT), bone scanning, magnetic resonance imaging (MRI), Positron Emission Tomography, PET-CT, and X-ray scanning. These techniques are combined with machine learning algorithms, data mining, and artificial intelligence-based detection techniques, which improve detection through efficient computing systems known as computer assisted diagnosis (CAD). Since practically all lung cancer screening and detection is dependent on image processing, this article will serve as a reference for aspiring researchers to understand the many detection strategies in effectively identifying lung cancer. Additionally, five distinct methods are evaluated and critically analysed, along with their benefits and drawbacks, taking into account the present and potential future developments in early lung cancer diagnosis for human survival.","PeriodicalId":497048,"journal":{"name":"Journal of Innovative Image Processing","volume":" 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138611812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Contrast Enhancement of Alzheimer’s MRI using Histogram Analysis 利用直方图分析增强阿尔茨海默氏症磁共振成像的对比度

Journal of Innovative Image Processing Pub Date : 2023-12-01 DOI: 10.36548/jiip.2023.4.003

Archana B, K. Kalirajan

引用次数: 0

Performance Evaluation of CNN Models for Alzheimer’s Disease Detection with MRI Scans 利用核磁共振成像扫描检测阿尔茨海默病的 CNN 模型性能评估

Journal of Innovative Image Processing Pub Date : 2023-12-01 DOI: 10.36548/jiip.2023.4.004

T. S. Sindhu, N. Kumaratharan, P. Anandan, P. Durga

{"title":"Performance Evaluation of CNN Models for Alzheimer’s Disease Detection with MRI Scans","authors":"T. S. Sindhu, N. Kumaratharan, P. Anandan, P. Durga","doi":"10.36548/jiip.2023.4.004","DOIUrl":"https://doi.org/10.36548/jiip.2023.4.004","url":null,"abstract":"The primary symptom of Alzheimer's disease is memory impairment, which is a neurodegenerative condition. The manifestation of these symptoms can be attributed to the impairment of the cerebral nerve responsible for cognitive functions such as learning, thinking and memory. Alzheimer’s disease is a prominent cause of mortality and lacks a definitive curve. However, appropriate medicinal interventions have demonstrated the potential to mitigate the progression and severity of the condition. This study presents the comparison of Convolutional Neural Network (CNN) models, namely ResNet50, VGG19 and VGG16 architectures, as an approach to construct an automated classification system for Alzheimer’s disease in future. The study utilises Magnetic Resonance Imaging (MRI) datasets to identify MRI datasets of individual with Alzheimer’s disease (AD), Cognitively normal (CN), mild cognitive impairment (MCI), early mild cognitive impairment (EMCI), and late mild cognitive impairment (LMCI). In the conducted experiment, the study achieved accuracy rates of 91.18% and 94.56% while utilising an epoch size of 2. The accuracy results indicate that the VGG16 model outperforms the ResNet50 model. The utilisation of automated Alzheimer’s disease classification holds potential as an auxiliary tool for healthcare professionals in determining the stage of Alzheimer’s disease hence facilitating the administration of suitable medicinal interventions.","PeriodicalId":497048,"journal":{"name":"Journal of Innovative Image Processing","volume":" 528","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138610879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identification of Image Forgeries using Machine Learning - A Review 利用机器学习识别图像伪造——综述

Journal of Innovative Image Processing Pub Date : 2023-09-01 DOI: 10.36548/jiip.2023.3.007

None Nagarathna C R, None Jayasri A, None Chandana S, None Amrutha A

引用次数: 0

A Hybrid Approach for Data Hiding using Twofish Algorithm and Compression Steganography Techniques 使用Twofish算法和压缩隐写技术的混合数据隐藏方法

Journal of Innovative Image Processing Pub Date : 2023-09-01 DOI: 10.36548/jiip.2023.3.003

None Merlin K, None Pradiksha S, None Deepa Lakshimi B, None Ramya G

{"title":"A Hybrid Approach for Data Hiding using Twofish Algorithm and Compression Steganography Techniques","authors":"None Merlin K, None Pradiksha S, None Deepa Lakshimi B, None Ramya G","doi":"10.36548/jiip.2023.3.003","DOIUrl":"https://doi.org/10.36548/jiip.2023.3.003","url":null,"abstract":"Data compression and encryption are essential components of information security, facilitating efficient data handling, reduced storage requirements, and secure data transmission. The system presents a novel hybrid data compression algorithm that combines lossy and lossless compression techniques, along with Twofish cryptography. The hybrid approach leverages the strengths of different compression techniques. First, the Huffman coding algorithm is employed to compress textual data efficiently. Subsequently, the cover image undergoes lossy compression using the Discrete Wavelet Transform (DWT) technique. To fortify data security, the Twofish algorithm offers robust and high-level encryption. The encrypted data is then embedded into the compacted cover image using the least significant bit (LSB) technique in a steganographic manner. In the evaluation phase, the system's performance is assessed using key metrics, bits per pixel, mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM). Comparative analysis with existing methodologies demonstrates the superior performance and methodological efficiency of the system. The results indicate that the hybrid approach strikes a balance between compression efficiency and data security, enabling faster data transmission over limited bandwidth connections and effectively utilizing storage media.","PeriodicalId":497048,"journal":{"name":"Journal of Innovative Image Processing","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135736541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Early Detection of Breast Cancer using Versatile Techniques - A Study 使用多种技术早期检测乳腺癌-一项研究

Journal of Innovative Image Processing Pub Date : 2023-09-01 DOI: 10.36548/jiip.2023.3.004

None Sanjay S Tippannavar, None Yashwanth S D, None Gayathri S, None Eshwari A Madappa

{"title":"Early Detection of Breast Cancer using Versatile Techniques - A Study","authors":"None Sanjay S Tippannavar, None Yashwanth S D, None Gayathri S, None Eshwari A Madappa","doi":"10.36548/jiip.2023.3.004","DOIUrl":"https://doi.org/10.36548/jiip.2023.3.004","url":null,"abstract":"Among all cancer types, breast cancer is the most prevalent. For females, it ranks as the second most common cause of cancer-related mortality. Every 1 person per 28 people in lifetime have a chance of developing breast cancer, according to statistics. Each year, it is estimated that over two million women encounter it. The high-risk group in India has an average age of 43–46 years, but in the west, women between the ages of 53 and 57 are more likely to get breast cancer. While there is no known cure for breast cancer, early detection and diagnosis significantly improves chances of survival. Treatment for breast cancer patients may be possible if the disease is identified and diagnosed early. Diagnosing benign from malignant tumours and determining whether a breast cancer case is early or progressed presents a number of challenges for cancer researchers. This study compares many methods for detecting breast cancer and provides a detailed analysis of each, highlighting the methods that are most accurate and economical. This study's primary goal is to comprehend the fundamental principles behind the various technologies used in breast cancer diagnosis. It is simple to save lives by spreading awareness of the latest and most varied detection and screening techniques.","PeriodicalId":497048,"journal":{"name":"Journal of Innovative Image Processing","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135737377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fourier Transform Driven Bioacoustics for Frequency-Based Pest Deterrence in Sustainable Agriculture 傅里叶变换驱动的生物声学在可持续农业中基于频率的害虫威慑

Journal of Innovative Image Processing Pub Date : 2023-09-01 DOI: 10.36548/jiip.2023.3.005

Neha Bhati

{"title":"Fourier Transform Driven Bioacoustics for Frequency-Based Pest Deterrence in Sustainable Agriculture","authors":"Neha Bhati","doi":"10.36548/jiip.2023.3.005","DOIUrl":"https://doi.org/10.36548/jiip.2023.3.005","url":null,"abstract":"The focus of this research is on using bioacoustics for frequency-based pest deterrence in sustainable agriculture, with the Fourier transform as the driving force. The critical need for new and improved methods of pest control in agricultural settings is addressed. This study, which makes use of cutting-edge technology, investigates how the Fourier Transform might be used as a useful instrument in the fight against pests. This research makes use of a cutting-edge algorithm for pest control; it's based on Fourier Transform bioacoustic analysis. By using the \"Insect Bioacoustic Signals (IBS) Dataset,\" this study reveals the algorithm's effectiveness in recognising and managing pests, as indicated by a remarkable classification accuracy of 93%. This study makes important contributions to the growing body of sustainable agriculture knowledge and has far-reaching consequences for the agricultural sector. A revolutionary new method of pest control is presented, with the potential to increase agricultural output and sustainability while decreasing crop losses. Specialised tools and libraries such as the Fast Fourier Transform (FFT) method from the NumPy toolkit for spectral analysis, Scikit-learn for machine learning approaches, and Librosa for audio signal processing, were used in this work to produce these encouraging outcomes. In conclusion, this research highlights the promise of bioacoustics based on the Fourier Transform to usher in a new era of environmentally responsible farming by effectively discouraging pests.","PeriodicalId":497048,"journal":{"name":"Journal of Innovative Image Processing","volume":"143 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135737161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Computer Aided Diagnosis (CAD) and Classification of Microbial and other Wide Range of Dermal Diseases using AI and Medical Image Processing 使用AI和医学图像处理的微生物和其他广泛皮肤疾病的计算机辅助诊断(CAD)和分类

Journal of Innovative Image Processing Pub Date : 2023-09-01 DOI: 10.36548/jiip.2023.3.006

None M. Sujitha, None N. Leela, None B. Kanimozhi

{"title":"Computer Aided Diagnosis (CAD) and Classification of Microbial and other Wide Range of Dermal Diseases using AI and Medical Image Processing","authors":"None M. Sujitha, None N. Leela, None B. Kanimozhi","doi":"10.36548/jiip.2023.3.006","DOIUrl":"https://doi.org/10.36548/jiip.2023.3.006","url":null,"abstract":"The human body's largest and most defensive organ is its skin. It covers internal organs and shields the human body from extraneous substances outside of it. Numerous illnesses can harm a person's skin caused by microbes such as bacteria, fungi, and viruses; for example, MRSA (methicillin-resistant Staphylococcus aureus) infection, Herpes zoster, Acne vulgaris, warts, eczema, psoriasis, and the fifth disease. It can also be damaged by carcinogenic and tumor-inducing agents, leading to skin cancer such as melanoma, which is more fatal and life-threatening to human life. Skin diseases can be diagnosed by blood tests, tissue sample collection (biopsy), and skin examination by dermatologists and experts. If non-expert doctors or laboratory technicians examine the skin, it can lead to medical errors and misdiagnosis. A proper and precise diagnosis and detection are required to treat the specific disease. This research aims to detect dermal diseases through sample images and classify and identify the cause of disease with greater accuracy and precision in a time- and cost-efficient way. This research uses medical processing algorithms such as pre-processing and segmentation of the diseased image and image classification algorithms such as deep learning, part of a neural network, to classify the diseased medical images.","PeriodicalId":497048,"journal":{"name":"Journal of Innovative Image Processing","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135737547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0