A deep transfer learning-based approaches for the detection and classification of acute lymphocytic leukemia using microscopic images

Traditionally, researchers, doctors, and hematologists have faced difficulties in making a timely diagnosis of leukemia. Since, unlike other malignancies, leukemia typically does not create a (tumor) that can be seen in imaging techniques like X-rays or CT scans. As a result the procedures available at medical diagnosis centres of leukemia consumes a lot of time. Influenced by the capabilities of artificial intelligence techniques diagnosing the disease, the paper introduces advanced CNN models which includes InceptionV3, DenseNet201, Xception, ResNet152V2, and two hybrid models, i.e., InceptionResNetV2 and XceptionInceptionResNetV2 for identifying and classifying normal and leukemia cancer cells. Accuracy, root mean square error, recall, precision, F1 score, and loss are used to evaluate all applied advanced learning models. The Acute lymphoblastic leukemia dataset, which is separated into two classes: normal cells and malignant leukemia cells, is used in this study. During the pre-processing stage, every image undergo enhancement and are visually shown to fetch the color channels in the shape of an RGB histogram. Later the images are augmented to produce regions of interest by generating extreme points and employing adaptive thresholding techniques before being provided to the applied models for training. During the experimentation, it was discovered that InceptionResNetV2 had the highest validation accuracy of 98.59%. Similarly, DenseNet201 had the highest precision (97.57%), followed by InceptionV3 with recall (95.77%) and F1 score (95.56%). Moreover, the confusion matrix has also been generated to obtain the models’ recall, precision, and F1 score values for different classes of the dataset.

A Review of Deep Learning‑Based Approaches for Detection and Diagnosis of Diverse Classes of Drugs

Artificial intelligence-based drug discovery has gained attention lately since it drastically cuts the time and money needed to produce new treatments. In recent years, a vast quantity of data in various formats has been made accessible in the medical field to analyse different health complications. Drug discovery aims to uncover possible novel medications using a multidisciplinary approach that includes biology, chemistry, and pharmacology. Traditional sentiment analysis methods count or repeat words in a text assigned sentiment ratings by an expert. Several outdated, ineffective old methodologies are utilized to forecast drug design and discovery. However, with the development of DL (deep learning), the traditional drug discovery method has been further simplified. In this work, we applied deep learning models, such as LSTM (Long short-term memory), GRU (Gated recurrent units), Bidirectional LSTM (BiLSTM), Bidirectional GRU(BiGRU), SimpleRNN, embedding+LSTM, embedding+GRU, embedding+GRU+dropout, embedding+conv1d+LSTM, and Embedding+Conv1d+GRU on a dataset of drug reviews. Furthermore, we used Adam and RMSprop, two optimizers, for each model, for increased optimization. This research focuses on categorizing medication reviews into positive and negative categories. The effectiveness of the different deep learning models was assessed using a wide range of performance measures. Experiments demonstrated that the GRU (Gated Recurrent Unit) generated exceptional validation dataset results. In addition, this study emphasizes the relevance of deep learning methods over traditional learning approaches in categorization.