Hybrid convolutional neural networks with SVM classifier for classification of skin cancer

Keerthana, Duggani; Venugopal, Vipin; Nath, Malaya Kumar; Mishra, Madhusudhan

doi:10.1016/j.bea.2022.100069

Cited by 121 publications

(46 citation statements)

References 24 publications

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“…Figure 7 is shown for the pictorial representation of performance comparison of our proposed with the existing algorithms of Stutz et al [26,27]. Table 1 is for numerical comparison the Table 1 is clearly shown that our proposed algorithm is provided the higher results than the existing methods of SVM and HMM.…”

Section: Resultsmentioning

confidence: 88%

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Prediction of Lungs Cancer in Medical Images Using Deep Learning Approach

Pachala¹,

Bojja²

2023

ISI

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In this advanced and fast-moving world, imaging techniques are the root of any medical diagnosis system. Encroachment in technology and machine learning methods may well aid radiologists in the verdict of tumors without any aggressive measures. CT images are mostly used to identify the deep defection of human being especially for lungs cancer. In this proposed system, we implemented the automatic prediction of lungs cancer in CT images using a new deep learning architecture named as Improved Cross Channel AlexNet (ICCAN). Segmentation of lungs cancer is performed via super pixel segmentation algorithm. This segmentation is used to remove the null regions in the CT images and also retain the features of tumor to make the system of efficient prediction. Once the image is properly segmented then it was categorized using our proposed deep learning network of ICCAN. Our proposed deep network is designed using transfer learning mechanism from the pretrained model of AlexNet. The simulation results are analyzed and compared the performance of proposed system with two different existing algorithms of HMM and SVM classification in terms of Accuracy. We achieved 99% of accuracy and F1-score of 99.74% which is higher than the previous implementations and also time complexity of our system is very low compared with HMM and SVM.

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Section: Resultsmentioning

confidence: 88%

“…These images are separated as 12 lakhs images for training, 50,000imagesfor validation, and 150,000 images for testing. The number of classes in all these images is 1000 [27]. The layer architecture of AlexNet is shown in Figure 3.…”

Section: A) Pretrained Alexnet Architecturementioning

confidence: 99%

Prediction of Lungs Cancer in Medical Images Using Deep Learning Approach

Pachala¹,

Bojja²

2023

ISI

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“…The identification and categorization of a wide range of skin cancers may be accomplished with the use of dermoscopy photographs [ 32 , 33 , 34 , 35 ]. Our method offers a full view of a particular site, which enables us to identify the disease, as well as interior areas that have been infected with it.…”

Section: Resultsmentioning

confidence: 99%

“…Keerthana et al [ 35 ] classified dermoscopy images as either benign or malignant cancers using two new hybrid CNN models, including an SVM algorithm at the output layer. The parameters extracted by the initial CNN model and the second CNN model are combined and passed to the SVM classifier.…”

Section: Literature Reviewmentioning

confidence: 99%

DSCC_Net: Multi-Classification Deep Learning Models for Diagnosing of Skin Cancer Using Dermoscopic Images

Tahir

Naeem

Malik

et al. 2023

Cancers

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Skin cancer is one of the most lethal kinds of human illness. In the present state of the health care system, skin cancer identification is a time-consuming procedure and if it is not diagnosed initially then it can be threatening to human life. To attain a high prospect of complete recovery, early detection of skin cancer is crucial. In the last several years, the application of deep learning (DL) algorithms for the detection of skin cancer has grown in popularity. Based on a DL model, this work intended to build a multi-classification technique for diagnosing skin cancers such as melanoma (MEL), basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanocytic nevi (MN). In this paper, we have proposed a novel model, a deep learning-based skin cancer classification network (DSCC_Net) that is based on a convolutional neural network (CNN), and evaluated it on three publicly available benchmark datasets (i.e., ISIC 2020, HAM10000, and DermIS). For the skin cancer diagnosis, the classification performance of the proposed DSCC_Net model is compared with six baseline deep networks, including ResNet-152, Vgg-16, Vgg-19, Inception-V3, EfficientNet-B0, and MobileNet. In addition, we used SMOTE Tomek to handle the minority classes issue that exists in this dataset. The proposed DSCC_Net obtained a 99.43% AUC, along with a 94.17%, accuracy, a recall of 93.76%, a precision of 94.28%, and an F1-score of 93.93% in categorizing the four distinct types of skin cancer diseases. The rates of accuracy for ResNet-152, Vgg-19, MobileNet, Vgg-16, EfficientNet-B0, and Inception-V3 are 89.32%, 91.68%, 92.51%, 91.12%, 89.46% and 91.82%, respectively. The results showed that our proposed DSCC_Net model performs better as compared to baseline models, thus offering significant support to dermatologists and health experts to diagnose skin cancer.

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“…There are 16 convolutional, pooling, and fully connected layers in VGG-16. 26 In contrast to the AlexNet architecture, the filters have a fixed size. As a result of extensive training, VGG-16 provides excellent accuracy even when dealing with datasets containing fewer images.…”

Section: Vgg-16mentioning

confidence: 99%

CVINet: A deep learning based model for the diagnosis of chronic venous insufficiency in lower extremity using infrared thermal images

Krishnan,

Muthu

2023

Int J Imaging Syst Tech

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Chronic venous insufficiency (CVI) is a venous disorder characterized by impaired blood flow from the lower extremities back to the heart, leading to various symptoms and complications. Accurate and timely diagnosis of CVI is critical for effective management and prevention of further complications. Deep learning (DL) models have shown results that are promising in the field of medical image analysis tasks over the past few years. This article presents CVINet, a enhanced DL‐based convolutional neural network (CNN) model that has been developed specifically for the purpose of diagnosing CVI utilizing thermal imaging data. The proposed CNN model extracts relevant features from input images using a multi‐layered architecture with convolutional layers and pooling layers. Following feature extraction, data are directed to fully connected layers for performing classification task. To enhance the performance of this model, we employ techniques such as dropout regularization and batch normalization. This model utilizes its ability to automatically learn discriminative features from raw thermal images. A large set of thermal images from both CVI patients and healthy individuals was acquired for training and testing of the model. A comparison between few conventional pre‐trained models was conducted, and the best performing model for classification was examined. Pre‐trained VGG‐16, EfficientNet‐B0, and ResNet‐152 models produced classification accuracies of 94.7%, 95.3%, and 95.8%, respectively. The Proposed CVINet model attained a classification accuracy of 96.8% on binary classification, demonstrating high performance compared with other state‐of‐the‐art networks. Overall, our proposed CNN model offers a promising solution for accurately classifying CVI and normal subjects based on thermal images which outperforms the diagnosis by clinician. The potential benefits include early detection of CVI and personalized treatment strategies that can improve patient outcomes. They improve the accuracy of patient diagnosis, reducing radiologist workload and providing them with a tool that can automatically assess the condition of the lower limb venous condition, lowering the risk of misdiagnosis. Furthermore, improving healthcare service quality and early detection can alter the course of the disease and reduce the morbidity and mortality rate of the disease.

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Hybrid convolutional neural networks with SVM classifier for classification of skin cancer

Cited by 121 publications

References 24 publications

Prediction of Lungs Cancer in Medical Images Using Deep Learning Approach

Prediction of Lungs Cancer in Medical Images Using Deep Learning Approach

DSCC_Net: Multi-Classification Deep Learning Models for Diagnosing of Skin Cancer Using Dermoscopic Images

CVINet: A deep learning based model for the diagnosis of chronic venous insufficiency in lower extremity using infrared thermal images

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