An optimized CNN based intelligent prognostics model for disease prediction and classification from Dermoscopy images

Tyagi, Amit Kumar; Mehra, Ritika

doi:10.1007/s11042-020-09074-3

Cited by 12 publications

(5 citation statements)

References 18 publications

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“…Thus, coarse hair is removed by the algorithm while preserving the features of the lesion [24]. Tyagi et al (2020) proposed an intelligent prognostic model for disease prediction and classification using a combination of CNN with particle swarm optimization (PSO) [25]. Ahmad et al (2021) discussed the generative adversarial networks (GANs) method for training a convolutional neural network on a balanced dataset [26].…”

Section: Related Workmentioning

confidence: 99%

Deep Learning and Machine Learning Techniques of Diagnosis Dermoscopy Images for Early Detection of Skin Diseases

Abunadi

Senan

2021

Electronics

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With the increasing incidence of severe skin diseases, such as skin cancer, endoscopic medical imaging has become urgent for revealing the internal and hidden tissues under the skin. Diagnostic information to help doctors make an accurate diagnosis is provided by endoscopy devices. Nonetheless, most skin diseases have similar features, which make it challenging for dermatologists to diagnose patients accurately. Therefore, machine and deep learning techniques can have a critical role in diagnosing dermatoscopy images and in the accurate early detection of skin diseases. In this study, systems for the early detection of skin lesions were developed. The performance of the machine learning and deep learning was evaluated on two datasets (e.g., the International Skin Imaging Collaboration (ISIC 2018) and Pedro Hispano (PH2)). First, the proposed system was based on hybrid features that were extracted by three algorithms: local binary pattern (LBP), gray level co-occurrence matrix (GLCM), and wavelet transform (DWT). Such features were then integrated into a feature vector and classified using artificial neural network (ANN) and feedforward neural network (FFNN) classifiers. The FFNN and ANN classifiers achieved superior results compared to the other methods. Accuracy rates of 95.24% for diagnosing the ISIC 2018 dataset and 97.91% for diagnosing the PH2 dataset were achieved using the FFNN algorithm. Second, convolutional neural networks (CNNs) (e.g., ResNet-50 and AlexNet models) were applied to diagnose skin diseases using the transfer learning method. It was found that the ResNet-50 model fared better than AlexNet. Accuracy rates of 90% for diagnosing the ISIC 2018 dataset and 95.8% for the PH2 dataset were reached using the ResNet-50 model.

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Section: Related Workmentioning

confidence: 99%

Deep Learning and Machine Learning Techniques of Diagnosis Dermoscopy Images for Early Detection of Skin Diseases

Abunadi

Senan

2021

Electronics

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“…In their own study, they have said that “There is no rule of thumb for setting these parameters.” which is not theoretically possible and thus can be considered as its drawback. On the other hand, the recent study done by Tyagi and Mehra [44] proposed the CNN based method for skin lesion classification; however, in their study, they are mentioning that “This study uses a dataset of 1000 dermoscopy skin lesion images of 545 BCCs and 455 non‐BCCs or benign images as the input sets. This dataset is taken from ISBI‐2016 Dataset and available on: http://www.isic-archive.com.” which cannot be true as dataset contains only benign and malignant cases have not disclosed their subcategories, and number of total images in the dataset is 900 for training and 379 for testing.…”

Section: Related Workmentioning

confidence: 99%

“…which is not theoretically possible and thus can be considered as its drawback. On the other hand, the recent study done by Tyagi and Mehra [44] proposed the CNN based method for skin lesion classification; however, in their study, they are mentioning that "This study uses a dataset of 1000 dermoscopy skin lesion images of 545 BCCs and 455 non-BCCs or benign images as the input sets. This dataset is taken from ISBI-2016 Dataset and available on: www.isic-archive.…”

Section: Related Workmentioning

confidence: 99%

Towards accurate classification of skin cancer from dermatology images

Gautam

Raman

2021

IET Image Processing

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Skin cancer is the most well-known disease found in the individuals who are exposed to the Sun's ultraviolet (UV) radiations. It is identified when skin tissues on the epidermis grow in an uncontrolled manner and appears to be of different colour than the normal skin tissues. This paper focuses on predicting the class of dermascopic images as benign and malignant. A new feature extraction method has been proposed to carry out this work which can extract relevant features from image texture. Local and gradient information from x and y directions of images has been utilized for feature extraction. After that images are classified using machine learning algorithms by using those extracted features. The efficacy of the proposed feature extraction method has been proved by conducting several experiments on the publicly available image dataset 2016 International Skin Imaging Collaboration (ISIC 2016). The classification results obtained by the method are also compared with state-of-the-art feature extraction methods which show that it performs better than others. The evaluation criteria used to obtain the results are accuracy, true positive rate (TPR) and false positive rate (FPR) where TPR and FPR are used for generating receiver operating characteristic curves.

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“…This strategic approach is essential for ensuring the attainment of results that exhibit high relevance to the field of precision medicine. Against this background, the survival models based on deep learning and PET or CT can adaptively learn high-level features from images with high predictive performance and realize end-to-end functions, which have gradually become the mainstream of deep survival models [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

An Innovative and Efficient Diagnostic Prediction Flow for Head and Neck Cancer: A Deep Learning Approach for Multi-Modal Survival Analysis Prediction Based on Text and Multi-Center PET/CT Images

Wang,

Zheng,

Han

et al. 2024

Diagnostics

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Objective: To comprehensively capture intra-tumor heterogeneity in head and neck cancer (HNC) and maximize the use of valid information collected in the clinical field, we propose a novel multi-modal image–text fusion strategy aimed at improving prognosis. Method: We have developed a tailored diagnostic algorithm for HNC, leveraging a deep learning-based model that integrates both image and clinical text information. For the image fusion part, we used the cross-attention mechanism to fuse the image information between PET and CT, and for the fusion of text and image, we used the Q-former architecture to fuse the text and image information. We also improved the traditional prognostic model by introducing time as a variable in the construction of the model, and finally obtained the corresponding prognostic results. Result: We assessed the efficacy of our methodology through the compilation of a multicenter dataset, achieving commendable outcomes in multicenter validations. Notably, our results for metastasis-free survival (MFS), recurrence-free survival (RFS), overall survival (OS), and progression-free survival (PFS) were as follows: 0.796, 0.626, 0.641, and 0.691. Our results demonstrate a notable superiority over the utilization of CT and PET independently, and exceed the result derived without the clinical textual information. Conclusions: Our model not only validates the effectiveness of multi-modal fusion in aiding diagnosis, but also provides insights for optimizing survival analysis. The study underscores the potential of our approach in enhancing prognosis and contributing to the advancement of personalized medicine in HNC.

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An optimized CNN based intelligent prognostics model for disease prediction and classification from Dermoscopy images

Cited by 12 publications

References 18 publications

Deep Learning and Machine Learning Techniques of Diagnosis Dermoscopy Images for Early Detection of Skin Diseases

Deep Learning and Machine Learning Techniques of Diagnosis Dermoscopy Images for Early Detection of Skin Diseases

Towards accurate classification of skin cancer from dermatology images

An Innovative and Efficient Diagnostic Prediction Flow for Head and Neck Cancer: A Deep Learning Approach for Multi-Modal Survival Analysis Prediction Based on Text and Multi-Center PET/CT Images

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