Bluish veil detection and lesion classification using custom deep learnable layers with explainable artificial intelligence (XAI).

Melanoma, one of the deadliest types of skin cancer, accounts for thousands of fatalities globally. The bluish, blue-whitish, or blue-white veil (BWV) is a critical feature for diagnosing melanoma, yet research into detecting BWV in dermatological images is limited. This study utilizes a non-annotated skin lesion dataset, which is converted into an annotated dataset using a proposed imaging algorithm (color threshold techniques) on lesion patches based on color palettes. A Deep Convolutional Neural Network (DCNN) is designed and trained separately on three individual and combined dermoscopic datasets, using custom layers instead of standard activation function layers. The model is developed to categorize skin lesions based on the presence of BWV. The proposed DCNN demonstrates superior performance compared to the conventional BWV detection models across different datasets. The model achieves a testing accuracy of 85.71 % on the augmented PH2 dataset, 95.00 % on the augmented ISIC archive dataset, 95.05 % on the combined augmented (PH2+ISIC archive) dataset, and 90.00 % on the Derm7pt dataset. An explainable artificial intelligence (XAI) algorithm is subsequently applied to interpret the DCNN's decision-making process about the BWV detection. The proposed approach, coupled with XAI, significantly improves the detection of BWV in skin lesions, outperforming existing models and providing a robust tool for early melanoma diagnosis.

Oral cancer prognosis based on clinicopathologic and genomic markers using a hybrid of feature selection and machine learning methods.

BACKGROUND: Machine learning techniques are becoming useful as an alternative approach to conventional medical diagnosis or prognosis as they are good for handling noisy and incomplete data, and significant results can be attained despite a small sample size. Traditionally, clinicians make prognostic decisions based on clinicopathologic markers. However, it is not easy for the most skilful clinician to come out with an accurate prognosis by using these markers alone. Thus, there is a need to use genomic markers to improve the accuracy of prognosis. The main aim of this research is to apply a hybrid of feature selection and machine learning methods in oral cancer prognosis based on the parameters of the correlation of clinicopathologic and genomic markers. RESULTS: In the first stage of this research, five feature selection methods have been proposed and experimented on the oral cancer prognosis dataset. In the second stage, the model with the features selected from each feature selection methods are tested on the proposed classifiers. Four types of classifiers are chosen; these are namely, ANFIS, artificial neural network, support vector machine and logistic regression. A k-fold cross-validation is implemented on all types of classifiers due to the small sample size. The hybrid model of ReliefF-GA-ANFIS with 3-input features of drink, invasion and p63 achieved the best accuracy (accuracy = 93.81%; AUC = 0.90) for the oral cancer prognosis. CONCLUSIONS: The results revealed that the prognosis is superior with the presence of both clinicopathologic and genomic markers. The selected features can be investigated further to validate the potential of becoming as significant prognostic signature in the oral cancer studies.

Prevalence and Risk Factors for Dangerous Abbreviations in Malaysian Electronic Clinical Notes.

Medical abbreviations can be misinterpreted and endanger patients' lives. This research is the first to investigate the prevalence of abbreviations in Malaysian electronic discharge summaries, where English is widely used, and elicit the risk factors associated with dangerous abbreviations. We randomly sampled and manually annotated 1102 electronic discharge summaries for abbreviations and their senses. Three medical doctors assigned a danger level to ambiguous abbreviations based on their potential to cause patient harm if misinterpreted. The predictors for dangerous abbreviations were determined using binary logistic regression. Abbreviations accounted for 19% (33,824) of total words; 22.6% (7640) of those abbreviations were ambiguous; and 52.3% (115) of the ambiguous abbreviations were labelled dangerous. Increased risk of danger occurs when abbreviations have more than two senses (OR = 2.991; 95% CI 1.586, 5.641), they are medication-related (OR = 6.240; 95% CI 2.674, 14.558), they are disorders (OR = 7.771; 95% CI 2.054, 29.409) and procedures (OR = 3.492; 95% CI 1.376, 8.860). Reduced risk of danger occurs when abbreviations are confined to a single discipline (OR = 0.519; 95% CI 0.278, 0.967). Managing abbreviations through awareness and implementing automated abbreviation detection and expansion would improve the quality of clinical documentation, patient safety, and the information extracted for secondary purposes.

Deep Learning for Plant Species Classification Using Leaf Vein Morphometric.

An automated plant species identification system could help botanists and layman in identifying plant species rapidly. Deep learning is robust for feature extraction as it is superior in providing deeper information of images. In this research, a new CNN-based method named D-Leaf was proposed. The leaf images were pre-processed and the features were extracted by using three different Convolutional Neural Network (CNN) models namely pre-trained AlexNet, fine-tuned AlexNet, and D-Leaf. These features were then classified by using five machine learning techniques, namely, Support Vector Machine (SVM), Artificial Neural Network (ANN), k-Nearest-Neighbor (k-NN), Naïve-Bayes (NB), and CNN. A conventional morphometric method computed the morphological measurements based on the Sobel segmented veins was employed for benchmarking purposes. The D-Leaf model achieved a comparable testing accuracy of 94.88 percent as compared to AlexNet (93.26 percent) and fine-tuned AlexNet (95.54 percent) models. In addition, CNN models performed better than the traditional morphometric measurements (66.55 percent). The features extracted from the CNN are found to be fitted well with the ANN classifier. D-Leaf can be an effective automated system for plant species identification as shown by the experimental results.

A genetic programming approach to oral cancer prognosis.

BACKGROUND: The potential of genetic programming (GP) on various fields has been attained in recent years. In bio-medical field, many researches in GP are focused on the recognition of cancerous cells and also on gene expression profiling data. In this research, the aim is to study the performance of GP on the survival prediction of a small sample size of oral cancer prognosis dataset, which is the first study in the field of oral cancer prognosis. METHOD: GP is applied on an oral cancer dataset that contains 31 cases collected from the Malaysia Oral Cancer Database and Tissue Bank System (MOCDTBS). The feature subsets that is automatically selected through GP were noted and the influences of this subset on the results of GP were recorded. In addition, a comparison between the GP performance and that of the Support Vector Machine (SVM) and logistic regression (LR) are also done in order to verify the predictive capabilities of the GP. RESULT: The result shows that GP performed the best (average accuracy of 83.87% and average AUROC of 0.8341) when the features selected are smoking, drinking, chewing, histological differentiation of SCC, and oncogene p63. In addition, based on the comparison results, we found that the GP outperformed the SVM and LR in oral cancer prognosis. DISCUSSION: Some of the features in the dataset are found to be statistically co-related. This is because the accuracy of the GP prediction drops when one of the feature in the best feature subset is excluded. Thus, GP provides an automatic feature selection function, which chooses features that are highly correlated to the prognosis of oral cancer. This makes GP an ideal prediction model for cancer clinical and genomic data that can be used to aid physicians in their decision making stage of diagnosis or prognosis.

Global Warming: Predicting OPEC Carbon Dioxide Emissions from Petroleum Consumption Using Neural Network and Hybrid Cuckoo Search Algorithm.

BACKGROUND: Global warming is attracting attention from policy makers due to its impacts such as floods, extreme weather, increases in temperature by 0.7°C, heat waves, storms, etc. These disasters result in loss of human life and billions of dollars in property. Global warming is believed to be caused by the emissions of greenhouse gases due to human activities including the emissions of carbon dioxide (CO2) from petroleum consumption. Limitations of the previous methods of predicting CO2 emissions and lack of work on the prediction of the Organization of the Petroleum Exporting Countries (OPEC) CO2 emissions from petroleum consumption have motivated this research. METHODS/FINDINGS: The OPEC CO2 emissions data were collected from the Energy Information Administration. Artificial Neural Network (ANN) adaptability and performance motivated its choice for this study. To improve effectiveness of the ANN, the cuckoo search algorithm was hybridised with accelerated particle swarm optimisation for training the ANN to build a model for the prediction of OPEC CO2 emissions. The proposed model predicts OPEC CO2 emissions for 3, 6, 9, 12 and 16 years with an improved accuracy and speed over the state-of-the-art methods. CONCLUSION: An accurate prediction of OPEC CO2 emissions can serve as a reference point for propagating the reorganisation of economic development in OPEC member countries with the view of reducing CO2 emissions to Kyoto benchmarks--hence, reducing global warming. The policy implications are discussed in the paper.

Long-term survival of nasopharyngeal carcinoma patients treated with adjuvant chemotherapy subsequent to conventional radical radiotherapy.

PURPOSE: To assess the long-term survival of patients with nasopharyngeal carcinoma (NPC) who were treated with conventional radical radiotherapy (RT) followed by adjuvant chemotherapy. METHODS AND MATERIALS: Ninety-one newly diagnosed patients with Stage III and IV (American Joint Committee on Cancer, 1988) NPC, seen at the University of Malaya Medical Center, Kuala Lumpur, Malaysia between January 1992 and May 1997, were treated with RT followed by adjuvant chemotherapy. The tumor dose was 70 Gy delivered in 35 fractions, 5 fractions weekly. Three cycles of chemotherapy, each consisting of 5-fluorouracil, 1 g/m(2)/d on Days 1-4 and cisplatin 100 mg/m(2) on Day 1, were administered 3 weeks after RT completion. Thirty-six patients had Stage II, 10 had Stage III, and 45 had Stage IV disease (AJCC 1997 staging system). RESULTS: After a median follow-up of 61 months, the 5-year overall survival rate for all 91 patients was 80.1%, the disease-free survival rate was 76%, and the locoregional control rate was 85%. The 3-year overall survival rate for Stage II was 94.3%; it was 80% for Stage III and 79.8% for Stage IV (p = 0.0108). The 3-year DFS rate for Stage II was 90%; it was 80% for Stage II and 65% for Stage IV. The rate of distant failure for Stage IV was 8.9%. CONCLUSION: Radical RT followed by adjuvant chemotherapy was effective in our patients with locoregionally advanced NPC. The long-term results appear encouraging, even for patients with Stage IV disease. This single institution experience deserves further investigation in prospective trials.