Cited by Hybrid methods for detection of starch in adulterated turmeric from colour images

Fusion of transfer learning models with LSTM for detection of breast cancer using ultrasound images DOI

Madhusudan G. Lanjewar, Kamini G. Panchbhai, L. B. Patle

et al.

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 169, P. 107914 - 107914

Published: Jan. 4, 2024

Language: Английский

Citations

Lung Cancer Classification using Optimized Attention-based Convolutional Neural Network with DenseNet-201 Transfer Learning Model on CT image DOI

G Mohandass,

G. Hari Krishnan,

D. Selvaraj

et al.

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 95, P. 106330 - 106330

Published: April 25, 2024

Language: Английский

Citations

Apnet: Lightweight network for apricot tree disease and pest detection in real-world complex backgrounds DOI

Minglang Li, Zhiyong Tao,

Wentao Yan

et al.

Plant Methods, Journal Year: 2025, Volume and Issue: 21(1)

Published: Jan. 9, 2025

Language: Английский

Citations

Portable system to detect starch adulteration in turmeric using NIR spectroscopy DOI

Madhusudan G. Lanjewar, Pranay P. Morajkar, Jivan S. Parab

et al.

Food Control, Journal Year: 2023, Volume and Issue: 155, P. 110095 - 110095

Published: Sept. 11, 2023

Language: Английский

Citations

Modified transfer learning frameworks to identify potato leaf diseases DOI

Madhusudan G. Lanjewar, Pranay P. Morajkar,

P Payaswini

et al.

Multimedia Tools and Applications, Journal Year: 2023, Volume and Issue: 83(17), P. 50401 - 50423

Published: Nov. 6, 2023

Language: Английский

Citations

Deep learning for lungs cancer detection: a review DOI

Rabia Javed,

Tahir Abbas,

Ali Haider Khan

et al.

Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(8)

Published: July 8, 2024

Abstract Although lung cancer has been recognized to be the deadliest type of cancer, a good prognosis and efficient treatment depend on early detection. Medical practitioners’ burden is reduced by deep learning techniques, especially Deep Convolutional Neural Networks (DCNN), which are essential in automating diagnosis classification diseases. In this study, we use variety medical imaging modalities, including X-rays, WSI, CT scans, MRI, thoroughly investigate techniques field classification. This study conducts comprehensive Systematic Literature Review (SLR) using for research, providing overview methodology, cutting-edge developments, quality assessments, customized approaches. It presents data from reputable journals concentrates years 2015–2024. solve difficulty manually identifying selecting abstract features images. includes wide range methods classifying but focuses most popular method, Network (CNN). CNN can achieve maximum accuracy because its multi-layer structure, automatic weights, capacity communicate local weights. Various algorithms shown with performance measures like precision, accuracy, specificity, sensitivity, AUC; consistently shows greatest accuracy. The findings highlight important contributions DCNN improving detection classification, making them an invaluable resource researchers looking gain greater knowledge learning’s function applications.

Language: Английский

Citations

Small size CNN (CAS-CNN), and modified MobileNetV2 (CAS-MODMOBNET) to identify cashew nut and fruit diseases DOI

Kamini G. Panchbhai, Madhusudan G. Lanjewar,

Vishant V. Malik

et al.

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: April 2, 2024

Language: Английский

Citations

Optimizing double-layered convolutional neural networks for efficient lung cancer classification through hyperparameter optimization and advanced image pre-processing techniques DOI

M. Mohamed Musthafa,

I. Manimozhi,

T R Mahesh

et al.

BMC Medical Informatics and Decision Making, Journal Year: 2024, Volume and Issue: 24(1)

Published: May 27, 2024

Abstract Lung cancer remains a leading cause of cancer-related mortality globally, with prognosis significantly dependent on early-stage detection. Traditional diagnostic methods, though effective, often face challenges regarding accuracy, early detection, and scalability, being invasive, time-consuming, prone to ambiguous interpretations. This study proposes an advanced machine learning model designed enhance lung stage classification using CT scan images, aiming overcome these limitations by offering faster, non-invasive, reliable tool. Utilizing the IQ-OTHNCCD dataset, comprising scans from various stages healthy individuals, we performed extensive preprocessing including resizing, normalization, Gaussian blurring. A Convolutional Neural Network (CNN) was then trained this preprocessed data, class imbalance addressed Synthetic Minority Over-sampling Technique (SMOTE). The model’s performance evaluated through metrics such as precision, recall, F1-score, ROC curve analysis. results demonstrated accuracy 99.64%, F1-score values exceeding 98% across all categories. SMOTE enhanced ability classify underrepresented classes, contributing robustness These findings underscore potential in transforming diagnostics, providing high classification, which could facilitate detection tailored treatment strategies, ultimately improving patient outcomes.

Language: Английский

Citations

Chest X-ray Images for Lung Disease Detection Using Deep Learning Techniques: A Comprehensive Survey DOI

Mohammed A. A. Al‐qaness,

Jie Zhu,

Dalal AL-Alimi

et al.

Archives of Computational Methods in Engineering, Journal Year: 2024, Volume and Issue: 31(6), P. 3267 - 3301

Published: Feb. 19, 2024

Language: Английский

Citations

CNN and transfer learning methods with augmentation for citrus leaf diseases detection using PaaS cloud on mobile DOI

Madhusudan G. Lanjewar, Jivan S. Parab

Multimedia Tools and Applications, Journal Year: 2023, Volume and Issue: 83(11), P. 31733 - 31758

Published: Sept. 19, 2023

Language: Английский

Citations