Skin Lesion Detection Using Hand-Crafted and DL-Based Features Fusion and LSTM

Mahum, Rabbia; Aladhadh, Suliman

doi:10.3390/diagnostics12122974

Cited by 17 publications

(8 citation statements)

References 51 publications

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“…Through tests on the HAM10000 datasets, they discovered that the MVT-based approach outperforms the most recent methods for classifying skin cancer. Deep learning algorithms can recognize melanoma from dermoscopy images [ 63 ]. Fuzzy GrabCut-stacked convolutional neural networks (GC-SCNNs) were employed for the imaging experiment.…”

Section: Related Workmentioning

confidence: 99%

An Improved Skin Lesion Classification Using a Hybrid Approach with Active Contour Snake Model and Lightweight Attention-Guided Capsule Networks

Behara,

Bhero,

Agee

2024

Diagnostics

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Skin cancer is a prevalent type of malignancy on a global scale, and the early and accurate diagnosis of this condition is of utmost importance for the survival of patients. The clinical assessment of cutaneous lesions is a crucial aspect of medical practice, although it encounters several obstacles, such as prolonged waiting time and misinterpretation. The intricate nature of skin lesions, coupled with variations in appearance and texture, presents substantial barriers to accurate classification. As such, skilled clinicians often struggle to differentiate benign moles from early malignant tumors in skin images. Although deep learning-based approaches such as convolution neural networks have made significant improvements, their stability and generalization continue to experience difficulties, and their performance in accurately delineating lesion borders, capturing refined spatial connections among features, and using contextual information for classification is suboptimal. To address these limitations, we propose a novel approach for skin lesion classification that combines snake models of active contour (AC) segmentation, ResNet50 for feature extraction, and a capsule network with a fusion of lightweight attention mechanisms to attain the different feature channels and spatial regions within feature maps, enhance the feature discrimination, and improve accuracy. We employed the stochastic gradient descent (SGD) optimization algorithm to optimize the model’s parameters. The proposed model is implemented on publicly available datasets, namely, HAM10000 and ISIC 2020. The experimental results showed that the proposed model achieved an accuracy of 98% and AUC-ROC of 97.3%, showcasing substantial potential in terms of effective model generalization compared to existing state-of-the-art (SOTA) approaches. These results highlight the potential for our approach to reshape automated dermatological diagnosis and provide a helpful tool for medical practitioners.

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Section: Related Workmentioning

confidence: 99%

An Improved Skin Lesion Classification Using a Hybrid Approach with Active Contour Snake Model and Lightweight Attention-Guided Capsule Networks

Behara,

Bhero,

Agee

2024

Diagnostics

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“…InceptionV3 [44], InceptionResV2 [45], XceptionNet [46], DenseNet-169 [15], MobileNetv2, E cientNet [47] and NasNet-Mobile [48], respectively. The comparative ndings are shown in Fig.…”

Section: Comparison With Existing DL Modelsmentioning

confidence: 99%

“…It is proven to be more e cient than ReLU and helps in learning complex patterns from videos achieving a high-recall rate. Furthermore, the extracted features from ResNet's layers are then classi ed using BiLSTM [15] layers that capture the meaningful attributes from input features and classify the frames effectively. The main contributions are as follows: a.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Deepfake Detection Using Robust Deep Learning Approch

Qadir

Mahum

El-Meligy

et al. 2023

Preprint

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The creation and manipulation of synthetic images have evolved rapidly, creating serious concerns about their effects on society. Although there have been various attempts to identify deep fake videos, these approaches are not universal. Identifying these misleading deepfakes is the first step in preventing them from following on social media sites. We introduce a unique deep-learning technique to identify fraudulent clips. Most deepfake identifiers currently focus on identifying face exchange, lip synchronous, expression modification, puppeteers, and other factors. However, exploring a consistent basis for all forms of fake video and images in real-time forensics is challenging. We propose a hybrid technique that takes input from videos of successive targeted frames, then feeds these to the ResNet-Swish-BiLSTM, an optimized convolutional BiLSTM-based residual network for training and classification. This proposed method helps identify artifacts in deepfake images that do not seem real. To assess the robustness of our proposed model, we used the open deepfake detection challenge dataset (DFDC) and Face Forensics deepfake collections (FF++.) We achieved 96.23% accuracy when using the FF + + digital record. In contrast, we attained 78.33% accuracy using the aggregated records from FF + + and DFDC. We performed extensive experiments and believe that our proposed method provides more significant results than existing techniques.

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“…In 61 a novel and reliable model based on feature fusion was proposed. In the first phase, preprocessing to remove the noise using a GF filter.…”

Section: Deep Learning Algorithms For Skin Cancer Diagnosismentioning

confidence: 99%

Skin cancer diagnosis using artificial intelligence methods: a review of the most recent research

dahdouh,

Abdelhakim Boudhir,

Ben Ahmed

2023

International Conference on Mathematical and Statistical Physics, Computational Science, Education and Communication (ICMSCE 20

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Skin Lesion Detection Using Hand-Crafted and DL-Based Features Fusion and LSTM

Cited by 17 publications

References 51 publications

An Improved Skin Lesion Classification Using a Hybrid Approach with Active Contour Snake Model and Lightweight Attention-Guided Capsule Networks

An Improved Skin Lesion Classification Using a Hybrid Approach with Active Contour Snake Model and Lightweight Attention-Guided Capsule Networks

An Efficient Deepfake Detection Using Robust Deep Learning Approch

Skin cancer diagnosis using artificial intelligence methods: a review of the most recent research

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