Satellite and Scene Image Classification Based on Transfer Learning and Fine Tuning of ResNet50

Shabbir, Amsa; Ali, Nouman; Ahmed, Jameel; Zafar, Bushra; Rasheed, Aqsa; Sajid, Muhammad; Ahmed, Ashfaq; Dar, Saadat Hanif

doi:10.1155/2021/5843816

Cited by 64 publications

(28 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors proposed the layers of a CNN model to fit a residual mapping instead of stacking each layer directly. Since it was introduced in 2015, ResNet was used in many classification tasks [ 26 , 27 ]. In healthcare, ResNet has been used with success in tasks such as pneumonia detection in chest Xray images [ 28 ], knee anterior cruciate ligament detection [ 29 ], breast cancer detection [ 30 ].…”

Section: Proposed Methodsmentioning

confidence: 99%

Malignant Bone Tumors Diagnosis Using Magnetic Resonance Imaging Based on Deep Learning Algorithms

et al. 2022

View full text Add to dashboard Cite

Background and Objectives: Malignant bone tumors represent a major problem due to their aggressiveness and low survival rate. One of the determining factors for improving vital and functional prognosis is the shortening of the time between the onset of symptoms and the moment when treatment starts. The objective of the study is to predict the malignancy of a bone tumor from magnetic resonance imaging (MRI) using deep learning algorithms. Materials and Methods: The cohort contained 23 patients in the study (14 women and 9 men with ages between 15 and 80). Two pretrained ResNet50 image classifiers are used to classify T1 and T2 weighted MRI scans. To predict the malignancy of a tumor, a clinical model is used. The model is a feed forward neural network whose inputs are patient clinical data and the output values of T1 and T2 classifiers. Results: For the training step, the accuracies of 93.67% for the T1 classifier and 86.67% for the T2 classifier were obtained. In validation, both classifiers obtained 95.00% accuracy. The clinical model had an accuracy of 80.84% for training phase and 80.56% for validation. The receiver operating characteristic curve (ROC) of the clinical model shows that the algorithm can perform class separation. Conclusions: The proposed method is based on pretrained deep learning classifiers which do not require a manual segmentation of the MRI images. These algorithms can be used to predict the malignancy of a tumor and on the other hand can shorten the time of their diagnosis and treatment process. While the proposed method requires minimal intervention from an imagist, it needs to be tested on a larger cohort of patients.

show abstract

Section: Proposed Methodsmentioning

confidence: 99%

Malignant Bone Tumors Diagnosis Using Magnetic Resonance Imaging Based on Deep Learning Algorithms

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The rest of the parameters include random seed number of 1, and stochastic gradient descendent ( sgd ) as a network optimiser because of its rapid convergence and fast training speed. We used the default value for most of the other learning parameters, such as momentum rate of 0.9 and learn rate factor 0.1, because previous research in [36] has established that they do not affect the performance of a network, whereas others [37, 38] showed that these default values produced reasonable results. The model performance was concurrently validated during the training process using a validation frequency of five (instead of default value 50) to reduce the computing time.…”

Section: Methodsmentioning

confidence: 99%

A hierarchical optimisation framework for pigmented lesion diagnosis

Huong

Tay

Gan

et al. 2022

CAAI Trans on Intel Tech

View full text Add to dashboard Cite

The study of training hyperparameters optimisation problems remains underexplored in skin lesion research. This is the first report of using hierarchical optimisation to improve computational effort in a four-dimensional search space for the problem. The authors explore training parameters selection in optimising the learning process of a model to differentiate pigmented lesions characteristics. In the authors' demonstration, pretrained GoogleNet is fine-tuned with a full training set by varying hyperparameters, namely epoch, mini-batch value, initial learning rate, and gradient threshold. The iterative search of the optimal global-local solution is by using the derivative-based method. The authors used non-parametric one-way ANOVA to test whether the classification accuracies differed for the variation in the training parameters. The authors identified the mini-batch size and initial learning rate as parameters that significantly influence the model's learning capability. The authors' results showed that a small fraction of combinations (5%) from constrained global search space, in contrarily to 82% at the local level, can converge with early stopping conditions. The mean (standard deviation, SD) validation accuracies increased from 78.4 (4.44)% to 82.9 (1.8)% using the authors' system. The fine-tuned model's performance measures evaluated on a testing dataset showed classification accuracy, precision, sensitivity, and specificity of 85.3%, 75.6%, 64.4%, and 97.2%, respectively. The authors' system achieves an overall better diagnosis performance than four state-of-the-art approaches via an improved search of parameters for a good adaptation of the model to the authors' dataset. The extended experiments also showed its superior performance consistency across different deep networks, where the overall classification accuracy increased by 5% with this technique. This approach reduces the risk of search being trapped in a suboptimal solution, and its use may be expanded to network architecture optimisation for enhanced diagnostic performance. K E Y W O R D S hierarchical, hyperparameter, optimisation, pigmented lesion, search | INTRODUCTIONDermatology is one of the most important fields of medicine that deals with diseases related to skin and cosmetic problems. This disease accounts for one-third of cancers diagnosed worldwide [1, 2] and will continue to be on the rise. Among the common challenges in the practice of dermatology is the exact diagnosis of skin cancer after a thorough physical examination, even at an early stage. Most skin lesions exhibit visually indiscernible characteristics [3], so morphological features that are looked after by dermatologists in their diagnosis include colour, boundary regularity, size, and texture of the examined skin [4, 5] using either ABCD rule, a seven-point checklist, or Menzies method [6,7]. This abnormal discolouration in the outer skin layer can be classified into benign and malignant skin lesions. While benign lesions are generally not invasiveThis is an open access arti...

show abstract

“…It has 20 classes in it. Training and testing ratio for SAT datasets is selected as 80:20, respectively, whereas for UCMD, it is 70:30 [65,66]. Figure 4 shows the basic process of image classification for CNN.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

“…e description such as image size, total number of images, images per class, and date of creation is referred to [65]. ere are a total of 21 distinctive scene categories with 100 images per class and dimensions of 256 × 256 pixels, as shown in dataset description table.…”

Section: Uc Merced Land Usementioning

confidence: 99%

See 1 more Smart Citation

Remote Sensing Image Classification: A Comprehensive Review and Applications

Mehmood

Shahzad

Zafar

et al. 2022

Mathematical Problems in Engineering

Self Cite

View full text Add to dashboard Cite

Remote sensing is mainly used to investigate sites of dams, bridges, and pipelines to locate construction materials and provide detailed geographic information. In remote sensing image analysis, the images captured through satellite and drones are used to observe surface of the Earth. The main aim of any image classification-based system is to assign semantic labels to captured images, and consequently, using these labels, images can be arranged in a semantic order. The semantic arrangement of images is used in various domains of digital image processing and computer vision such as remote sensing, image retrieval, object recognition, image annotation, scene analysis, content-based image analysis, and video analysis. The earlier approaches for remote sensing image analysis are based on low-level and mid-level feature extraction and representation. These techniques have shown good performance by using different feature combinations and machine learning approaches. These earlier approaches have used small-scale image dataset. The recent trends for remote sensing image analysis are shifted to the use of deep learning model. Various hybrid approaches of deep learning have shown much better results than the use of a single deep learning model. In this review article, a detailed overview of the past trends is presented, based on low-level and mid-level feature representation using traditional machine learning concepts. A summary of publicly available image benchmarks for remote sensing image analysis is also presented. A detailed summary is presented at the end of each section. An overview regarding the current trends of deep learning models is presented along with a detailed comparison of various hybrid approaches based on recent trends. The performance evaluation metrics are also discussed. This review article provides a detailed knowledge related to the existing trends in remote sensing image classification and possible future research directions.

show abstract

Satellite and Scene Image Classification Based on Transfer Learning and Fine Tuning of ResNet50

Cited by 64 publications

References 63 publications

Malignant Bone Tumors Diagnosis Using Magnetic Resonance Imaging Based on Deep Learning Algorithms

Malignant Bone Tumors Diagnosis Using Magnetic Resonance Imaging Based on Deep Learning Algorithms

A hierarchical optimisation framework for pigmented lesion diagnosis

Remote Sensing Image Classification: A Comprehensive Review and Applications

Contact Info

Product

Resources

About