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Blood cancer has emerged as a growing concern over the past decade, necessitating early diagnosis for timely and effective treatment. The present diagnostic method, which involves a battery of tests and medical experts, is costly and time-consuming. For this reason, it is crucial to establish an automated diagnostic system for accurate predictions. A particular field of focus in medical research is the use of machine learning and leukemia microarray gene data for blood cancer diagnosis. Even with a great deal of research, more improvements are needed to reach the appropriate levels of accuracy and efficacy. This work presents a supervised machine-learning algorithm for blood cancer prediction. This work makes use of the 22,283-gene leukemia microarray gene data. Chi-squared (Chi2) feature selection methods and the synthetic minority oversampling technique (SMOTE)-Tomek resampling is used to overcome issues with imbalanced and high-dimensional datasets. To balance the dataset for each target class, SMOTE-Tomek creates synthetic data, and Chi2 chooses the most important features to train the learning models from 22,283 genes. A novel weighted convolutional neural network (CNN) model is proposed for classification, utilizing the support of three separate CNN models. To determine the importance of the proposed approach, extensive experiments are carried out on the datasets, including a performance comparison with the most advanced techniques. Weighted CNN demonstrates superior performance over other models when coupled with SMOTE-Tomek and Chi2 techniques, achieving a remarkable 99.9% accuracy. Results from k-fold cross-validation further affirm the supremacy of the proposed model. [ABSTRACT FROM AUTHOR]

Cancer is a life-threatening disease and has witnessed a substantial increase during the past few years. With a further expected increase in the future, it poses a huge challenge for the medical industry to design accurate detection approaches. Earlier and accurate detection of these cancers increases the surviving chances of patients. Computer-aided diagnosis (CAD) can provide significant help in assisting medical experts to make timely and accurate diagnoses. Deep learning models show great promise but are limited by the need for larger datasets for training and higher computational complexity. This study proposes a resource-efficient EfficienetNetB4 model that incorporates local binary pattern features to enhance the detection accuracy of cancer. The proposed approach integrates histopathological images and texture-based features with EfficientNetB4. Extensive experiments involving several pre-trained convolutional neural network variants are carried out. Results show the superior results of the proposed EfficientNetB4 model with a 99.8% accuracy. In addition, precision, recall, and F1 scores are 99.9% each thereby indicating the exceptional results for cancer detection exceeding the existing state-of-the-art approaches. Using a 5-fold cross-validation provides an average 99.88% accuracy further validating the performance of EfficientNetB4. Furthermore, the use of Shapley additive explanations helps comprehend the results and increases the transparency of the decision-making process. This study potentially contributes to further research in CAD-based resource-efficient cancer detection. [ABSTRACT FROM AUTHOR]