Integrating Transformer and Bidirectional Long Short-Term Memory for Intelligent Breast Cancer Detection from Histopathology Biopsy Images

Breast cancer is a significant threat to the global population,affecting not only women but also a threat to the entire population.With recent advancements in digital pathology,Eosin and hematoxylin images provide enhanced clarity in examiningmicroscopic features of breast tissues based on their staining properties.Early cancer detection facilitates the quickening of the therapeutic process,thereby increasing survival rates.The analysis made by medical professionals,especially pathologists,is time-consuming and challenging,and there arises a need for automated breast cancer detection systems.The upcoming artificial intelligence platforms,especially deep learning models,play an important role in image diagnosis and prediction.Initially,the histopathology biopsy images are taken from standard data sources.Further,the gathered images are given as input to the Multi-Scale Dilated Vision Transformer,where the essential features are acquired.Subsequently,the features are subjected to the Bidirectional Long Short-Term Memory(Bi-LSTM)for classifying the breast cancer disorder.The efficacy of the model is evaluated using divergent metrics.When compared with other methods,the proposed work reveals that it offers impressive results for detection.

Optimal IoT Based Improved Deep Learning Model for Medical Image Classification

Recently medical image classification plays a vital role in medical image retrieval and computer-aided diagnosis system.Despite deep learning has proved to be superior to previous approaches that depend on handcrafted features;it remains difficult to implement because of the high intra-class variance and inter-class similarity generated by the wide range of imaging modalities and clinical diseases.The Internet of Things(IoT)in healthcare systems is quickly becoming a viable alternative for delivering high-quality medical treatment in today’s e-healthcare systems.In recent years,the Internet of Things(IoT)has been identified as one of the most interesting research subjects in the field of health care,notably in the field of medical image processing.For medical picture analysis,researchers used a combination of machine and deep learning techniques as well as artificial intelligence.These newly discovered approaches are employed to determine diseases,which may aid medical specialists in disease diagnosis at an earlier stage,giving precise,reliable,efficient,and timely results,and lowering death rates.Based on this insight,a novel optimal IoT-based improved deep learning model named optimization-driven deep belief neural network(ODBNN)is proposed in this article.In context,primarily image quality enhancement procedures like noise removal and contrast normalization are employed.Then the preprocessed image is subjected to feature extraction techniques in which intensity histogram,an average pixel of RGB channels,first-order statistics,Grey Level Co-Occurrence Matrix,Discrete Wavelet Transform,and Local Binary Pattern measures are extracted.After extracting these sets of features,the May Fly optimization technique is adopted to select the most relevant features.The selected features are fed into the proposed classification algorithm in terms of classifying similar input images into similar classes.The proposed model is evaluated in terms of accuracy,precision,recall,and f-measure.The investigation evident the performance of incorporating optimization techniques for medical image classification is better than conventional techniques.

Generative Deep Belief Model for Improved Medical Image Segmentation

Medical image assessment is based on segmentation at its fundamental stage.Deep neural networks have been more popular for segmentation work in recent years.However,the quality of labels has an impact on the training performance of these algorithms,particularly in the medical image domain,where both the interpretation cost and inter-observer variation are considerable.For this reason,a novel optimized deep learning approach is proposed for medical image segmentation.Optimization plays an important role in terms of resources used,accuracy,and the time taken.The noise in the raw medical image are processed using Quasi-Continuous Wavelet Transform(QCWT).Then,feature extraction and selection are done after the pre-processing of the image.The features are optimally selected by the Golden Eagle Optimization(GEO)method.Specifically,the processed image is segmented accurately using the proposed Generative Heap Belief Network(GHBN)technique.The execution of this research is done on MATLAB software.According to the results of the experiments,the proposed framework is superior to current techniques in terms of segmentation performance with a valid accuracy of 99%,which is comparable to the other methods.