Intelligent Beetle Antenna Search with Deep Transfer Learning Enabled Medical Image Classification Model

Recently,computer assisted diagnosis(CAD)model creation has become more dependent on medical picture categorization.It is often used to identify several conditions,including brain disorders,diabetic retinopathy,and skin cancer.Most traditional CAD methods relied on textures,colours,and forms.Because many models are issue-oriented,they need a more substantial capacity to generalize and cannot capture high-level problem domain notions.Recent deep learning(DL)models have been published,providing a practical way to develop models specifically for classifying input medical pictures.This paper offers an intelligent beetle antenna search(IBAS-DTL)method for classifying medical images facilitated by deep transfer learning.The IBAS-DTL model aims to recognize and classify medical pictures into various groups.In order to segment medical pictures,the current IBASDTLM model first develops an entropy based weighting and first-order cumulative moment(EWFCM)approach.Additionally,the DenseNet-121 techniquewas used as a module for extracting features.ABASwith an extreme learning machine(ELM)model is used to classify the medical photos.A wide variety of tests were carried out using a benchmark medical imaging dataset to demonstrate the IBAS-DTL model’s noteworthy performance.The results gained indicated the IBAS-DTL model’s superiority over its pre-existing techniques.

A Novel Deep Neural Network for Intracranial Haemorrhage Detection and Classification

Data fusion is one of the challenging issues,the healthcare sector is facing in the recent years.Proper diagnosis from digital imagery and treatment are deemed to be the right solution.Intracerebral Haemorrhage(ICH),a condition characterized by injury of blood vessels in brain tissues,is one of the important reasons for stroke.Images generated by X-rays and Computed Tomography(CT)are widely used for estimating the size and location of hemorrhages.Radiologists use manual planimetry,a time-consuming process for segmenting CT scan images.Deep Learning(DL)is the most preferred method to increase the efficiency of diagnosing ICH.In this paper,the researcher presents a unique multi-modal data fusion-based feature extraction technique with Deep Learning(DL)model,abbreviated as FFE-DL for Intracranial Haemorrhage Detection and Classification,also known as FFEDL-ICH.The proposed FFEDL-ICH model has four stages namely,preprocessing,image segmentation,feature extraction,and classification.The input image is first preprocessed using the Gaussian Filtering(GF)technique to remove noise.Secondly,the Density-based Fuzzy C-Means(DFCM)algorithm is used to segment the images.Furthermore,the Fusion-based Feature Extraction model is implemented with handcrafted feature(Local Binary Patterns)and deep features(Residual Network-152)to extract useful features.Finally,Deep Neural Network(DNN)is implemented as a classification technique to differentiate multiple classes of ICH.The researchers,in the current study,used benchmark Intracranial Haemorrhage dataset and simulated the FFEDL-ICH model to assess its diagnostic performance.The findings of the study revealed that the proposed FFEDL-ICH model has the ability to outperform existing models as there is a significant improvement in its performance.For future researches,the researcher recommends the performance improvement of FFEDL-ICH model using learning rate scheduling techniques for DNN.

An Optimal Lempel Ziv Markov Based Microarray Image Compression Algorithm

In the recent years,microarray technology gained attention for concurrent monitoring of numerous microarray images.It remains a major challenge to process,store and transmit such huge volumes of microarray images.So,image compression techniques are used in the reduction of number of bits so that it can be stored and the images can be shared easily.Various techniques have been proposed in the past with applications in different domains.The current research paper presents a novel image compression technique i.e.,optimized Linde–Buzo–Gray(OLBG)with Lempel Ziv Markov Algorithm(LZMA)coding technique called OLBG-LZMA for compressing microarray images without any loss of quality.LBG model is generally used in designing a local optimal codebook for image compression.Codebook construction is treated as an optimizationissue and can be resolved with the help of Grey Wolf Optimization(GWO)algorithm.Once the codebook is constructed by LBGGWO algorithm,LZMA is employed for the compression of index table and raise its compression efficiency additionally.Experiments were performed on high resolution Tissue Microarray(TMA)image dataset of 50 prostate tissue samples collected from prostate cancer patients.The compression performance of the proposed coding esd compared with recently proposed techniques.The simulation results infer that OLBG-LZMA coding achieved a significant compression performance compared to other techniques.

Optimal Deep Convolution Neural Network for Cervical Cancer Diagnosis Model

Biomedical imaging is an effective way of examining the internal organ of the human body and its diseases.An important kind of biomedical image is Pap smear image that iswidely employed for cervical cancer diagnosis.Cervical cancer is a vital reason for increased women’s mortality rate.Proper screening of pap smear images is essential to assist the earlier identification and diagnostic process of cervical cancer.Computer-aided systems for cancerous cell detection need to be developed using deep learning(DL)approaches.This study introduces an intelligent deep convolutional neural network for cervical cancer detection and classification(IDCNN-CDC)model using biomedical pap smear images.The proposed IDCNN-CDC model involves four major processes such as preprocessing,segmentation,feature extraction,and classification.Initially,the Gaussian filter(GF)technique is applied to enhance data through noise removal process in the Pap smear image.The Tsallis entropy technique with the dragonfly optimization(TE-DFO)algorithm determines the segmentation of an image to identify the diseased portions properly.The cell images are fed into the DL based SqueezeNet model to extract deeplearned features.Finally,the extracted features fromSqueezeNet are applied to the weighted extreme learning machine(ELM)classification model to detect and classify the cervix cells.For experimental validation,the Herlev database is employed.The database was developed at Herlev University Hospital(Denmark).The experimental outcomes make sure that higher performance of the proposed technique interms of sensitivity,specificity,accuracy,and F-Score.