Search · four archives

Results for InceptionV3

or ask it as a questionAsk the stacks →

ArchivesarXiv PubMed bioRxiv chemRxiv

WhenAny time Past month Past year Past 5 years

OrderMost relevant Most cited Newest

12 papers · ranked by Valyu relevance

01
PPubMed29 May 2024
A deep learning approach for electric motor fault diagnosis based on modified InceptionV3
Lifu Xu, Soo Siang Teoh, Haidi Ibrahim
Electric motors are essential equipment widely employed in various sectors. However, factors such as prolonged operation, environmental conditions, and inadequate maintenance make electric motors prone to various failures. In this study, we propose a thermography-based motor fault detection method based on InceptionV3…
84relevance
02
AarXiv15 Mar 2024
Monkeypox disease recognition model based on improved SE-InceptionV3
Junzhuo Chen, Zonghan Lu, Shitong Kang
In the wake of the global spread of monkeypox, accurate disease recognition has become crucial. This study introduces an improved SE-InceptionV3 model, embedding the SENet module and incorporating L2 regularization into the InceptionV3 framework to enhance monkeypox disease detection. Utilizing the Kaggle monkeypox…
79relevance
03
PPubMed22 Apr 2025
Enhanced breast cancer diagnosis using modified InceptionNet-V3: a deep learning approach for ultrasound image classification Chelloug et al. 10.3389/fphys.2025.1558001
Samia Allaoua Chelloug, Abduljabbar S. Ba Mahel, Rana Alnashwan, Ahsan Rafiq + 2 more
'Ahsan Rafiq' 'Mohammed Saleh Ali Muthanna' 'Ahmed Aziz'] Introduction Breast cancer (BC) is a malignant neoplasm that originates in the mammary gland’s cellular structures and remains one of the most prevalent cancers among women, ranking second in cancer-related mortality after lung cancer. Early and accurate…
79relevance
04
PPubMed7 Oct 2022
Deep learning-based segmentation and classification of leaf images for detection of tomato plant disease
Muhammad Shoaib, Tariq Hussain, Babar Shah, Ihsan Ullah + 3 more
'Sayyed Mudassar Shah' 'Farman Ali' 'Sang Hyun Park'] Plants contribute significantly to the global food supply. Various Plant diseases can result in production losses, which can be avoided by maintaining vigilance. However, manually monitoring plant diseases by agriculture experts and botanists is time-consuming…
78relevance
05
PPubMed7 May 2026
Enhanced rice leaf disease classification via contour-driven segmentation and optimized deep transfer learning architectures Enhanced rice leaf disease classification
Ummer Shakeel, Muhammad Asif Habib, Muhammad Yasir, Muhammad Umar Chaudhry + 5 more
Pakistan is the fourth-largest rice producer and the fifth-largest exporter worldwide. Timely disease detection remains challenging due to the scale of cultivation and reliance on manual monitoring. Developing reliable, ongoing computerized systems for plant health management is essential for efficient disease control.…
78relevance
06
AarXiv2023
On the Steganographic Capacity of Selected Learning Models
Rishit Agrawal, Kelvin Jou, Tanush Obili, Daksh Parikh + 5 more
'Samarth Prajapati' 'Yash Seth' 'Charan Sridhar' 'Nathan Zhang' 'Mark Stamp'] Machine learning and deep learning models are potential vectors for various attack scenarios. For example, previous research has shown that malware can be hidden in deep learning models. Hiding information in a learning model can be viewed as…
77relevance
07
AarXiv25 Jul 2026
When Less Is More: A Controlled Benchmark of Lightweight CNNs for Satellite Land-Cover Segmentation on DeepGlobe
Atiq Ur Rehman, Joseph Michael Donovan
High-resolution satellite imagery is the backbone of good land-cover classification, and without that, environmental monitoring, urban planning, and sustainable resource management all fall short. Deep learning architectures perform well in semantic segmentation, but the efficiency-accuracy trade-off across classical…
73relevance
08
PPubMed11 Apr 2022
COV-DLS: Prediction of COVID-19 from X-Rays Using Enhanced Deep Transfer Learning Techniques
Vijay Kumar, Anis Zarrad, Rahul Gupta, Omar Cheikhrouhou
In this paper, modifications in neoteric architectures such as VGG16, VGG19, ResNet50, and InceptionV3 are proposed for the classification of COVID-19 using chest X-rays. The proposed architectures termed “COV-DLS” consist of two phases: heading model construction and classification. The heading model construction…
72relevance
09
AarXiv2022
PCONet: A Convolutional Neural Network Architecture to Detect Polycystic Ovary Syndrome (PCOS) from Ovarian Ultrasound Images
A. K. M. Salman Hosain, Md Humaion Kabir Mehedi, Irteza Enan Kabir
—Polycystic Ovary Syndrome (PCOS) is an endrocrinological dysfunction prevalent among women of reproductive age. PCOS is a combination of syndromes caused by an excess of androgens — a group of sex hormones — in women. Syndromes including acne, alopecia, hirsutism, hyperandrogenaemia, oligoovulation, etc. are caused by…
71relevance
10
AarXiv17 Dec 2022
Multi-class Skin Cancer Classification Architecture Based on Deep Convolutional Neural Network
Mst Shapna Akter, Hossain Shahriar, Sweta Sneha, Alfredo Cuzzocrea
—Skin cancer is a deadly disease. Melanoma is a type of skin cancer responsible for the high mortality rate. Early detection of skin cancer can enable patients to treat the disease and minimize the death rate. Skin cancer detection is challenging since different types of skin lesions share high similarities. This paper…
71relevance
11
PPubMed19 Dec 2025
Squeeze-and-Excitation Enhanced Convolutional Neural Networks for Multi-class Pneumonia Classification on Chest Radiographs
Alexander Muacevic, John R Adler, Kian A Huang, Haris K Choudhary + 2 more
This study compared two convolutional neural network (CNN) architectures, ResNet50V2 and InceptionV3, each enhanced with squeeze-and-excitation (SE) attention mechanisms, for automated classification of chest X-rays into normal, pneumonia-bacterial, pneumonia-viral, and COVID-19 categories. A total of 9,208…
70relevance
12
BbioRxiv10 Sep 2023
Machine learning to identify clinically relevant *Candida* yeast species
Shamanth A. Shankarnarayan, Daniel A. Charlebois
Fungal infections, especially due to Candida species, are on the rise. Multi-drug resistant organism such as Candida auris are difficult and time consuming to identify accurately. Machine learning is increasingly being used in health care, especially in medical imaging. In this study, we evaluated the effectiveness of…
67relevance

Results for InceptionV3

A deep learning approach for electric motor fault diagnosis based on modified InceptionV3

Monkeypox disease recognition model based on improved SE-InceptionV3

Enhanced breast cancer diagnosis using modified InceptionNet-V3: a deep learning approach for ultrasound image classification Chelloug et al. 10.3389/fphys.2025.1558001

Deep learning-based segmentation and classification of leaf images for detection of tomato plant disease

Enhanced rice leaf disease classification via contour-driven segmentation and optimized deep transfer learning architectures Enhanced rice leaf disease classification

On the Steganographic Capacity of Selected Learning Models

When Less Is More: A Controlled Benchmark of Lightweight CNNs for Satellite Land-Cover Segmentation on DeepGlobe

COV-DLS: Prediction of COVID-19 from X-Rays Using Enhanced Deep Transfer Learning Techniques

PCONet: A Convolutional Neural Network Architecture to Detect Polycystic Ovary Syndrome (PCOS) from Ovarian Ultrasound Images

Multi-class Skin Cancer Classification Architecture Based on Deep Convolutional Neural Network

Squeeze-and-Excitation Enhanced Convolutional Neural Networks for Multi-class Pneumonia Classification on Chest Radiographs

Machine learning to identify clinically relevant Candida yeast species

A deep learning approach for electric motor fault diagnosis based on modified InceptionV3

Monkeypox disease recognition model based on improved SE-InceptionV3

Enhanced breast cancer diagnosis using modified InceptionNet-V3: a deep learning approach for ultrasound image classification Chelloug et al. 10.3389/fphys.2025.1558001

Deep learning-based segmentation and classification of leaf images for detection of tomato plant disease

Enhanced rice leaf disease classification via contour-driven segmentation and optimized deep transfer learning architectures Enhanced rice leaf disease classification

On the Steganographic Capacity of Selected Learning Models

When Less Is More: A Controlled Benchmark of Lightweight CNNs for Satellite Land-Cover Segmentation on DeepGlobe

COV-DLS: Prediction of COVID-19 from X-Rays Using Enhanced Deep Transfer Learning Techniques

PCONet: A Convolutional Neural Network Architecture to Detect Polycystic Ovary Syndrome (PCOS) from Ovarian Ultrasound Images

Multi-class Skin Cancer Classification Architecture Based on Deep Convolutional Neural Network

Squeeze-and-Excitation Enhanced Convolutional Neural Networks for Multi-class Pneumonia Classification on Chest Radiographs

Machine learning to identify clinically relevant Candida yeast species

Results for InceptionV3

A deep learning approach for electric motor fault diagnosis based on modified InceptionV3

Monkeypox disease recognition model based on improved SE-InceptionV3

Enhanced breast cancer diagnosis using modified InceptionNet-V3: a deep learning approach for ultrasound image classification Chelloug et al. 10.3389/fphys.2025.1558001

Deep learning-based segmentation and classification of leaf images for detection of tomato plant disease

Enhanced rice leaf disease classification via contour-driven segmentation and optimized deep transfer learning architectures Enhanced rice leaf disease classification

On the Steganographic Capacity of Selected Learning Models

When Less Is More: A Controlled Benchmark of Lightweight CNNs for Satellite Land-Cover Segmentation on DeepGlobe

COV-DLS: Prediction of COVID-19 from X-Rays Using Enhanced Deep Transfer Learning Techniques

PCONet: A Convolutional Neural Network Architecture to Detect Polycystic Ovary Syndrome (PCOS) from Ovarian Ultrasound Images

Multi-class Skin Cancer Classification Architecture Based on Deep Convolutional Neural Network

Squeeze-and-Excitation Enhanced Convolutional Neural Networks for Multi-class Pneumonia Classification on Chest Radiographs

Machine learning to identify clinically relevant *Candida* yeast species

A deep learning approach for electric motor fault diagnosis based on modified InceptionV3

Monkeypox disease recognition model based on improved SE-InceptionV3

Enhanced breast cancer diagnosis using modified InceptionNet-V3: a deep learning approach for ultrasound image classification Chelloug et al. 10.3389/fphys.2025.1558001

Deep learning-based segmentation and classification of leaf images for detection of tomato plant disease

Enhanced rice leaf disease classification via contour-driven segmentation and optimized deep transfer learning architectures Enhanced rice leaf disease classification

On the Steganographic Capacity of Selected Learning Models

When Less Is More: A Controlled Benchmark of Lightweight CNNs for Satellite Land-Cover Segmentation on DeepGlobe

COV-DLS: Prediction of COVID-19 from X-Rays Using Enhanced Deep Transfer Learning Techniques

PCONet: A Convolutional Neural Network Architecture to Detect Polycystic Ovary Syndrome (PCOS) from Ovarian Ultrasound Images

Multi-class Skin Cancer Classification Architecture Based on Deep Convolutional Neural Network

Squeeze-and-Excitation Enhanced Convolutional Neural Networks for Multi-class Pneumonia Classification on Chest Radiographs

Machine learning to identify clinically relevant *Candida* yeast species

Machine learning to identify clinically relevant Candida yeast species

Machine learning to identify clinically relevant Candida yeast species