Artificial intelligence in medical diagnostics: A review from a South African context

Behara, Kavita; Bhero, Ernest; Agee, John T.; Gonela, Vinay

doi:10.1016/j.sciaf.2022.e01360

Cited by 9 publications

(8 citation statements)

References 25 publications

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“…In the Image Preprocessing phase, Mean Square Error (MSE) [ 105 ], Peak Signal-to-Noise Ratio (PSNR) [ 105 ] and Structural Similarity Index (SSIM) [ 106 ] were used. To assess the classification performance of the model, Balanced Accuracy Score (BAS), accuracy, recall, precision, specificity, and F1-Score [ 74 , 104 , 105 , 106 , 107 ] were used as evaluation metrics, which are developed from True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN) predictions. The confusion matrix and AUC-ROC plot are used for the graphical presentation of the accuracy of the proposed improved model [ 108 ].…”

Section: Resultsmentioning

confidence: 99%

Skin Lesion Synthesis and Classification Using an Improved DCGAN Classifier

2023

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The prognosis for patients with skin cancer improves with regular screening and checkups. Unfortunately, many people with skin cancer do not receive a diagnosis until the disease has advanced beyond the point of effective therapy. Early detection is critical, and automated diagnostic technologies like dermoscopy, an imaging device that detects skin lesions early in the disease, are a driving factor. The lack of annotated data and class-imbalance datasets makes using automated diagnostic methods challenging for skin lesion classification. In recent years, deep learning models have performed well in medical diagnosis. Unfortunately, such models require a substantial amount of annotated data for training. Applying a data augmentation method based on generative adversarial networks (GANs) to classify skin lesions is a plausible solution by generating synthetic images to address the problem. This article proposes a skin lesion synthesis and classification model based on an Improved Deep Convolutional Generative Adversarial Network (DCGAN). The proposed system generates realistic images using several convolutional neural networks, making training easier. Scaling, normalization, sharpening, color transformation, and median filters enhance image details during training. The proposed model uses generator and discriminator networks, global average pooling with 2 × 2 fractional-stride, backpropagation with a constant learning rate of 0.01 instead of 0.0002, and the most effective hyperparameters for optimization to efficiently generate high-quality synthetic skin lesion images. As for the classification, the final layer of the Discriminator is labeled as a classifier for predicting the target class. This study deals with a binary classification predicting two classes—benign and malignant—in the ISIC2017 dataset: accuracy, recall, precision, and F1-score model classification performance. BAS measures classifier accuracy on imbalanced datasets. The DCGAN Classifier model demonstrated superior performance with a notable accuracy of 99.38% and 99% for recall, precision, F1 score, and BAS, outperforming the state-of-the-art deep learning models. These results show that the DCGAN Classifier can generate high-quality skin lesion images and accurately classify them, making it a promising tool for deep learning-based medical image analysis.

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Section: Resultsmentioning

confidence: 99%

Skin Lesion Synthesis and Classification Using an Improved DCGAN Classifier

2023

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“…AI-powered automation speeds up processes such as sample processing, image analysis, and data interpretation. This not only shortens the diagnostic turnaround time but also allows healthcare professionals to concentrate on the most challenging aspects of patient care [ 19 , 22 ].…”

Section: Reviewmentioning

confidence: 99%

Applications of Artificial Intelligence in Microbial Diagnosis

Shelke,

Badge,

Bankar

2023

Cureus

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The diagnosis is an important factor in healthcare care, and it is essential to identify microorganisms that cause infections and diseases. The application of artificial intelligence (AI) systems can improve disease management, drug development, antibiotic resistance prediction, and epidemiological monitoring in the field of microbial diagnosis. AI systems can quickly and accurately detect infections, including new and drug-resistant strains, and enable early detection of antibiotic resistance and improved diagnostic techniques. The application of AI in bacterial diagnosis focuses on the speed, precision, and identification of pathogens and the ability to predict antibiotic resistance.

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“…However, CNNs have some limitations, such as not being able to represent spatial relationships between the features, sensitivity to noises [ 26 , 27 ] and limitations in generalizing to new data due to downsampling layers of CNN pooling layers, leading to data loss [ 28 , 29 , 30 ]. In addition, spatial information, and instantiation parameters (such as the position of low-level features to one another, deformation, and texture information) are not transferable in convolutional neural networks [ 30 ].…”

Section: Introductionmentioning

confidence: 99%

“… Feature Extraction : Failure to incorporate crucial spatial relationships among characteristics such as incorrect region features, healthy region features, and extra features that are necessary for the classification purpose [ 27 ]. Time-Consuming : Certain classification techniques may require a substantial amount of annotated data to operate optimally, which can be costly and time-consuming, especially for less frequent or specialized forms of skin cancer [ 28 ]. Lack of Interpretability : Understanding decisions and how to interpret the retrieved features is not always easy.…”

Section: Introductionmentioning

confidence: 99%

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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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Artificial intelligence in medical diagnostics: A review from a South African context

Cited by 9 publications

References 25 publications

Skin Lesion Synthesis and Classification Using an Improved DCGAN Classifier

Skin Lesion Synthesis and Classification Using an Improved DCGAN Classifier

Applications of Artificial Intelligence in Microbial Diagnosis

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

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