Pixel Mapping Research Articles

Integrating plot‐based and remotely sensed data to map vegetation types in a New Zealand warm‐temperate rainforest

AbstractQuestions(1) What can be learned by extending a national classification into unsampled forest types? (2) Are both remotely sensed and environmental predictors needed to model and map associations? (3) For mapping, are LiDAR‐generated canopy structure parameters or reflectance from spectral imagery more useful? (4) How can we assess uncertainty of a final map?LocationWarawara Forest, Northland, New Zealand.MethodsWe sampled 205 vegetation plots and assigned them to an existing national classification using the fuzzy classification framework of noise clustering. Plots too distinct to be assigned were used to define new associations. We produced spatial models of each association using boosted regression trees. Predictors included 11 environmental, 11 canopy reflectance, and 17 canopy structure variables. We created a composite map by assigning each map pixel to the association with the highest occurrence probability. We evaluated uncertainty by examining locations where no class was predicted with probability above 0.2 and by creating a confusion map based on entropy.ResultsForty‐five plots were assigned to six of 79 existing national associations and 147 plots were used to define two new forest associations. Three shrubland types are widespread nationally, whereas two young forest types are northern. Three mature forest types are narrowly distributed nationally, with the new “High‐elevation hardwood forest” largely restricted to Warawara Forest. Three associations were mapped using remotely sensed predictors alone, whereas two also required environmental predictors. Overall, canopy reflectance predictors explained more deviance than canopy structure. Examining locations where no association was predicted well and where multiple associations were predicted equally showed areas mapped as younger forests to have greatest uncertainty.ConclusionsIn answering our questions, we present a vegetation classification and map for Warawara Forest that provides a framework to guide the indigenous people's management responses to threats to valued communities and their species.

Formula omitted] cooling in the jovian aurora: Juno remote sensing observations and modeling

Cooling of Jupiter's auroral thermosphere by H3+ radiation to space is one of the processes controlling the energy balance in the auroral upper atmosphere. The UltraViolet Spectrograph (UVS) and the Jupiter InfraRed Auroral Mapper (JIRAM) on board Juno have observed the Jovian polar aurora from its polar orbit since August 2016. The UVS instrument measures the H2 Lyman and Werner bands whose brightness is a proxy of the precipitated auroral electron flux. The 3.3–3.6 μm spectral window of the JIRAM L-band imager maps the H3+ thermal radiance with unprecedented spatial resolution. Comparison of concurrent observations indicates that the morphological features are similar in the two spectral regions but differences are also observed in the spatial intensity contrast. We compare the total (direct and indirect) particle heating rate and the cooling by H3+ radiation derived from four pairs of simultaneous UVS and JIRAM images. The total auroral cooling power H3+ is in the range 2–4 terawatts in both hemispheres. In all cases, the H3+ cooling in the aurora is found to range between 0.45 and 0.67 time less than the particle collisional heating. In a second step, we use the ultraviolet H2 brightness and FUV color ratio to derive the characteristics of the electron precipitation and model the H3+ radiance for each UVS map pixel. The comparison of the H3+ modeled radiance map with the JIRAM observations shows general good agreement with some local differences. The four spatially integrated H3+ cooling power from the model are in very good agreement with the JIRAM values. These results are important constraints for global magnetosphere-ionosphere-atmosphere models.

Nature-inspired saccadic-like electrical stimulation paradigm promotes sustained retinal ganglion cell responses by spatiotemporally alternating activation of contiguous multi-electrode patterns

Objective. Retinal electrical stimulation using multi-electrode arrays (MEAs) aims to restore visual object perception in blind patients. However, the rate and duration of the artificial visual sensations are limited due to the rapid response decay of the stimulated neurons. Hence, we investigated a novel nature-inspired saccadic-like stimulation paradigm (biomimetic) to evoke sustained retinal responses. For implementation, the macroelectrode was replaced by several contiguous microelectrodes and activated non-simultaneously but alternating topologically. Approach. MEAs with hexagonally arranged electrodes were utilized to simulate and record mouse retinal ganglion cells (RGCs). Two shapes were presented electrically using MEAs: a 6e-hexagon (six hexagonally arranged 10 µm electrodes; 6e-hexagon diameter: 80 µm) and a double-bar (180 µm spaced, 320 µm in length). Electrodes of each shape were activated in three different modes (simultaneous, circular, and biomimetic (‘zig-zag’)), stimulating at different frequencies (1–20 Hz). Main results. The biomimetic stimulation generated enhanced RGC responses increasing the activity rate by 87.78%. In the spatiotemporal context, the electrical representation of the 6e-hexagon produced sustained and local RGC responses (∼130 µm corresponding to ∼2.5° of the human visual angle) for up to 90 s at 10 Hz stimulation and resolved the electrically presented double-bar. In contrast, during conventional simultaneous stimulation, the responses were poor and declined within seconds. Similarly, the applicability of the biomimetic mode for retinal implants (7 × 8 pixels) was successfully demonstrated. An object shape impersonating a smile was presented electrically, and the recorded data were used to emulate the implant’s performance. The spatiotemporal pixel mapping of the activity produced a complete retinal image of the smile. Significance. The application of electrical stimulation in the biomimetic mode produced locally enhanced RGC responses with significantly reduced fading effects and yielded advanced spatiotemporal performance reflecting the presented electrode shapes in the mapped activity imprint. Therefore, it is likely that the RGC responses persist long enough to evoke visual perception and generate a seamless image, taking advantage of the flicker fusion. Hence, replacing the implant’s macroelectrodes with microelectrodes and their activation in a topologically alternating biomimetic fashion may overcome the patient’s perceptual image fading, thereby enhancing the spatiotemporal characteristics of artificial vision.

Characterizing the calibration domain of remote sensing models using convex hulls

• We used a convex hull approach to assess model validity domain. • Convex hulls allow identifying unreliable model predictions. • At low sampling intensity, higher proportion of extrapolation are observed. • Prediction biases increase with extrapolation distances from the calibration domain. • This approach could help model development, as a complementary tool used in model quality assessment. The ever-increasing availability of remote sensing data allows production of forest attributes maps, which are usually made using model-based approaches. These map products are sensitive to various bias sources, including model extrapolation. To identify, over a case study forest, the proportion of extrapolated predictions, we used a convex hull method applied to the auxiliary data space of an airborne laser scanning (ALS) flight. The impact of different sampling efforts was also evaluated. This was done by iteratively thinning a set of 487 systematic plots using nested sub-grids allowing to divide the sample by two at each level. The analysis were conducted for all alternative samples and evaluated against 56 independent validation plots. Residuals of the extrapolated validation plots were computed and examined as a function of their distance to the model calibration domain. Extrapolation was also characterized for the pixels of the area of interest (AOI) to upscale at population level. Results showed that the proportion of extrapolated pixels greatly reduced with an increasing sampling effort. It reached a plateau (ca. 20% extrapolation) with a sampling intensity of ca. 250-calibration plots. This contrasts with results on model’s root mean squared error (RMSE), which reached a plateau at a much lower sampling intensity. This result emphasizes the fact that with a low sampling effort, extrapolation risk remains high, even at a relatively low RMSE. For all attributes examined (i.e. , stand density, basal area, and quadratic mean diameter) estimations were generally found to be biased for validation plots that were extrapolated. The method allows an easy identification of map pixels that are out of the calibration domain, making it an interesting tool to evaluate model transferability over an area of interest (AOI). It could also serve to compare “competing” models at a variable selection phase. From a model calibration perspective, it could serve a posteriori , to evaluate areas (in the auxiliary space) that merit further sampling efforts to improve model reliability.

RETRACTED: Design and development of SER-UNet model for glaucoma image analysis

Glaucoma is a life-threatening disease that must be diagnosed early before it causes blindness. It is a dangerous disease and challenging for ophthalmologists. Retinal diseases can be detected at earlier stages with the help of analyzing fundus images of the retina and through the retinal vessel segmentation process. However, traditional fully convolutional neural network-based equivalents have major drawbacks in segmentation such as a bifurcation breakup in the vascular map and lessening pixel connectivity of vessels. To overcome this drawback, we present the squeeze excitation residual UNet (SER-UNet) model for vessel segmentation. The proposed model uses a new type of residual block called SER residual blocks for vessel segmentation. Initially, the fundus image is read and downsampled by converting the input image into vector values. Then, it conducts segmentation by adding attention mechanism and residual structure into convolution blocks to find vessel regions accurately and aggregate the tiny vessel characteristics. It helps segment the image of the glaucoma affected region in the retina. Together with a pixelwise cross-entropy loss function, it shows excellent performance on fundus image segmentation. The performance of the proposed method is assessed with an accuracy of 98.90% and 98.31%, respectively, using the DRIVE and STARE datasets, respectively.

Separated Contrastive Learning for Organ-at-Risk and Gross-Tumor-Volume Segmentation with Limited Annotation

Automatic delineation of organ-at-risk (OAR) and gross-tumor-volume (GTV) is of great significance for radiotherapy planning. However, it is a challenging task to learn powerful representations for accurate delineation under limited pixel (voxel)-wise annotations. Contrastive learning at pixel-level can alleviate the dependency on annotations by learning dense representations from unlabeled data. Recent studies in this direction design various contrastive losses on the feature maps, to yield discriminative features for each pixel in the map. However, pixels in the same map inevitably share semantics to be closer than they actually are, which may affect the discrimination of pixels in the same map and lead to the unfair comparison to pixels in other maps. To address these issues, we propose a separated region-level contrastive learning scheme, namely SepaReg, the core of which is to separate each image into regions and encode each region separately. Specifically, SepaReg comprises two components: a structure-aware image separation (SIS) module and an intra- and inter-organ distillation (IID) module. The SIS is proposed to operate on the image set to rebuild a region set under the guidance of structural information. The inter-organ representation will be learned from this set via typical contrastive losses cross regions. On the other hand, the IID is proposed to tackle the quantity imbalance in the region set as tiny organs may produce fewer regions, by exploiting intra-organ representations. We conducted extensive experiments to evaluate the proposed model on a public dataset and two private datasets. The experimental results demonstrate the effectiveness of the proposed model, consistently achieving better performance than state-of-the-art approaches. Code is available at https://github.com/jcwang123/Separate_CL.

SCSNet: An Efficient Paradigm for Learning Simultaneously Image Colorization and Super-resolution

In the practical application of restoring low-resolution gray-scale images, we generally need to run three separate processes of image colorization, super-resolution, and dows-sampling operation for the target device. However, this pipeline is redundant and inefficient for the independent processes, and some inner features could have been shared. Therefore, we present an efficient paradigm to perform Simultaneously Image Colorization and Super-resolution (SCS) and propose an end-to-end SCSNet to achieve this goal. The proposed method consists of two parts: colorization branch for learning color information that employs the proposed plug-and-play Pyramid Valve Cross Attention (PVCAttn) module to aggregate feature maps between source and reference images; and super-resolution branch for integrating color and texture information to predict target images, which uses the designed Continuous Pixel Mapping (CPM) module to predict high-resolution images at continuous magnification. Furthermore, our SCSNet supports both automatic and referential modes that is more flexible for practical application. Abundant experiments demonstrate the superiority of our method for generating authentic images over state-of-the-art methods, e.g., averagely decreasing FID by 1.8 and 5.1 compared with current best scores for automatic and referential modes, respectively, while owning fewer parameters (more than x2) and faster running speed (more than x3).

No rain in the world: A novel residual deep attention network for single image rain removal

Clear images are generally desirable in high-level computer vision algorithms which are mostly deployed outdoors. However, affected by the changeable weather in the real world, images are inevitably contaminated by rain streaks. Deep convolutional neural networks (CNNs) have shown significant potential in rain streaks removal. The performance of most existing CNN-based deraining methods is often enhanced by stacking vanilla convolutional layers and some other methods use dilated convolution which can only model local pixel relations to provide the necessary but limited receptive field. Therefore, long-range contextual information is rarely considered for this specific task, thus, deraining a single image remains challenging problem. To address the above problem, an effective residual deep attention network (RDANet) for single image rain removal is proposed. Specifically, we design a strong basic unit that contains dilated convolution, spatial and channel attention module (SCAM) simultaneously. As contextual information is very important for rain removal, the proposed basic unit can capture global long-distance dependencies among pixels in feature maps and model feature relations across channels. Compared with a single dilated convolution, the spatial and channel attention enhance the feature expression ability of the network. Moreover, some previous works have proven that the no-rain information in a rain image will be missing during deraining. To enrich the detailed information in the clean images, we present a residual feature processing group (RFPG) that contains several source skip connections to inject rainy shallow source information into each basic unit. In summary, our model can effectively handle complicated long rain streaks in spatial and the outputs of the network can retain most of the details of the original rain images. Experiments demonstrate the superiority of our RDANet over state-of-the-art methods in terms of both quantitative metrics and visual quality on both synthetic and real rainy images.

Broccoli microgreens treated with CaCl2 solution absorb calcium atoms and accumulate them as Ca(II) hydrated ions

Broccoli microgreens treated with 10 mM CaCl2 aqueous solution were investigated to clarify whether they absorb calcium (Ca) atoms and what chemical states absorbed Ca species take. X-ray fluorescence (XRF) measurements using synchrotron radiation were performed to map Ca concentrations in cotyledon (seed leaf) of broccoli microgreens. The mapping pixel size was 20 μm × 20 μm. The broccoli microgreens treated with 10 mM CaCl2 aqueous solution show Ca concentrated areas in the cotyledon. On the other hand, no Ca concentrated areas were observed for the cotyledon of the other broccoli microgreens that were treated with purified water. The Ca chemical state of the Ca concentrated areas was studied by x-ray absorption near edge structure (XANES) spectra. The obtained XANES spectrum of the Ca concentrated area shows the characteristic features of CaCl2 aqueous solution, and it does not correspond to the XANES spectrum of CaCl2 powders.

An adaptive pixel mapping based approach for reversible data hiding in encrypted images

Reversible data hiding (RDH) is a widely studied research domain due to the extensive applications in medical image transmission, military image transmission, and cloud computing. In this paper, we propose a novel RDH scheme in encrypted images which is having a high embedding rate without affecting the bit error rate. To perform data hiding, all the non-overlapping pixel sub-arrays from the encrypted image will be considered in a predefined order, and one-bit from the secret message can be hidden in each of the pixel sub-arrays through a controlled pixel mapping technique. The pixels in a selected sub-array P will be split into two groups: odd pixels and even pixels according to the parity of the index of the pixels. All the even pixels will be replaced by a new pixel value based on a predefined mapping function to hide a secret bit 1. To embed bit-value 0, no need to do any modifications in the pixel sub-array P. A new correlation measure between adjacent pixels is introduced in this manuscript to extract the secret message bit and to restore the original image. All the simulations of the proposed scheme are done on the standard images from USC-SIPI image dataset. The results show that the proposed scheme achieved a very good embedding rate without affecting the bit error rate or image recoverability.

Deep-learning-based projection-domain breast thickness estimation for shape-prior iterative image reconstruction in digital breast tomosynthesis.

Digital breast tomosynthesis (DBT) is a technique that can overcome the shortcomings of conventional X-ray mammography and can be effective for the early screening of breast cancer. The compression of the breast is essential during the DBT imaging. However, since the periphery of the breast cannot be compressed to a constant value, nonuniformity of thickness and in-plane shape variation happen. These cause inconvenience in diagnosis, scatter correction, and breast density estimation. In this study, we propose a deep-learning-based methodology for projection-domain breast thickness estimation and demonstrate a shape-prior iterative DBT image reconstruction. We prepared the Euclidean distance map, the thickness map, and the thickness corrected image of the simulated breast projections for thickness and shape estimation. Each pixel of the Euclidean distance map denotes a distance to the closest skin-line. The thickness map is defined as a conceptual projection of ideal breast support that differentiates the inner and outer regions of the breast phantom. The thickness projection map thus represents the X-ray path lengths of a homogeneous breast phantom. We generated the thickness corrected image by dividing the projection image by the thickness map in a pixel-wise manner. We developed a convolutional neural network for thickness estimation and correction. The network utilizes a projection image and a Euclidean distance image together as a dual input. An estimated breast thickness map is then used for constructing the breast shape mask by use of the discrete algebraic reconstruction technique. The proposed network effectively corrected the breast thickness in various simulation situations. Low normalized root-mean-squared error (1.976%) and high structural similarity (99.997%) indicated a good agreement between the network-generated thickness corrected image and the ground truth image. Compared to the existing methods and simple single-input network, the proposed method showed outperformance in breast thickness estimation and accordingly in breast shape recovery for various numerical phantoms without provoking any significant artifact. We have demonstrated that the uniformity of voxel value has improved by the inclusion of a shape prior for the iterative DBT reconstruction. We presented a novel deep-learning-based breast thickness correction and a shape reconstruction method. This approach to estimating the true thickness map and the shape of the breast undergoing compression can benefit various fields such as improvement of diagnostic breast images, scatter correction, material decomposition, and breast density estimation.

Field-of-view enhanced integral imaging with dual prism arrays based on perspective-dependent pixel mapping.

A field-of-view (FOV)-enhanced integral imaging system is proposed by the combined use of micro-lens array (MLA) and dual-prism array (DPA). The MLA coupled with a DPA virtually function as a new type of the MLA whose FOV is much more increased than that of the original MLA, which enables the capturing of perspective-expanded elemental image arrays (EIAs) of input 3-D scenes and the FOV-enhanced reconstruction of them. For its practical operation, a two-step digital process called perspective-dependent pixel-mapping (PDPM) is also presented. With this PDPM method, picked-up EIAs with a couple of MLAs and DPAs are remapped into the new forms of EIAs to be properly reconstructed in the conventional integral imaging system. Operational performances of the proposed system are ray-optically analyzed. In addition, the feasibility of the proposed system is also confirmed from the computational and optical experiments with test 3-D objects on the implemented prototype. Experimental results finally show a two-times increase of the FOV range of the proposed system when it is compared with that of the conventional system.

A pseudo-random pixel mapping with weighted mesh graph approach for reversible data hiding in encrypted image

A pseudo-random pixel mapping with weighted mesh graph approach for reversible data hiding in encrypted image

A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps

Over the past decade, several global maps of above-ground biomass (AGB) have been produced, but they exhibit significant differences that reduce their value for climate and carbon cycle modelling, and also for national estimates of forest carbon stocks and their changes. The number of such maps is anticipated to increase because of new satellite missions dedicated to measuring AGB. Objective and consistent methods to estimate the accuracy and uncertainty of AGB maps are therefore urgently needed. This paper develops and demonstrates a framework aimed at achieving this. The framework provides a means to compare AGB maps with AGB estimates from a global collection of National Forest Inventories and research plots that accounts for the uncertainty of plot AGB errors. This uncertainty depends strongly on plot size, and is dominated by the combined errors from tree measurements and allometric models (inter-quartile range of their standard deviation (SD) = 30–151 Mg ha−1). Estimates of sampling errors are also important, especially in the most common case where plots are smaller than map pixels (SD = 16–44 Mg ha−1). Plot uncertainty estimates are used to calculate the minimum-variance linear unbiased estimates of the mean forest AGB when averaged to 0.1∘. These are used to assess four AGB maps: Baccini (2000), GEOCARBON (2008), GlobBiomass (2010) and CCI Biomass (2017). Map bias, estimated using the differences between the plot and 0.1∘ map averages, is modelled using random forest regression driven by variables shown to affect the map estimates. The bias model is particularly sensitive to the map estimate of AGB and tree cover, and exhibits strong regional biases. Variograms indicate that AGB map errors have map-specific spatial correlation up to a range of 50–104 km, which increases the variance of spatially aggregated AGB map estimates compared to when pixel errors are independent. After bias adjustment, total pantropical AGB and its associated SD are derived for the four map epochs. This total becomes closer to the value estimated by the Forest Resources Assessment after every epoch and shows a similar decrease. The framework is applicable to both local and global-scale analysis, and is available at https://github.com/arnanaraza/PlotToMap. Our study therefore constitutes a major step towards improved AGB map validation and improvement.

Boundary-Aware Arbitrary-Shaped Scene Text Detector With Learnable Embedding Network

Benefiting from the popularity of deep learning theory, scene text detection algorithms have developed rapidly in recent years. Methods representing text region by text segmentation map are proved to capture arbitrary-shaped text in a more flexible and accurate way. However, such segmentation-based methods are prone to be disturbed by the text-like background patterns (like the fence, grass, etc.), which generally suffer from imprecise boundary detail problem. In this paper, LEMNet is proposed to handle the imprecise boundary problem by guiding the generation of text boundary based on a priori constraint. In the training stage, Boundary Segmentation Branch is firstly constructed to predict coarse boundary mask for each text instance. Then, through mapping pixels into an embedding space, the proposed Pixel Embedding Branch makes the embedding representation of boundary points learn to be more similar, meanwhile enlarging the characteristic distance between background points and boundary points. During inference, noise in the coarse boundary segmentation map can be effectively suppressed by a Noisy Point Suppression Algorithm among pixel embedding vectors. In this way, LEMNet can generate a more precise boundary description of text regions. To further enhance the distinguishability of boundary features, we propose a Context Enhancement Module to capture feature interactions in different representation subspaces, in which features are parallelly performed attention and concatenated to generate enhanced features. Extensive experiments are conducted over four challenging datasets, which demonstrate the effectiveness of LEMNet. Specifically, LEMNet achieves F-measure of 85.2%, 87.6% and 85.2% on CTW1500, Total-Text and MSRA-TD500 respectively, which is the latest SOTA.

Change Detection From Synthetic Aperture Radar Images via Dual Path Denoising Network

Benefited from the rapid and sustainable development of synthetic aperture radar (SAR) sensors, change detection from SAR images has received increasing attentions over the past few years. Existing unsupervised deep learning-based methods have made great efforts to exploit robust feature representations, but they consume much time to optimize parameters. Besides, these methods use clustering to obtain pseudolabels for training, and the pseudolabeled samples often involve errors, which can be considered as “label noise.” To address these issues, we propose a dual path denoising network (DPDNet) for SAR image change detection. In particular, we introduce the random label propagation to clean the label noise involved in preclassification. We also propose the distinctive patch convolution for feature representation learning to reduce the time consumption. Specifically, the attention mechanism is used to select distinctive pixels in the feature maps, and patches around these pixels are selected as convolution kernels. Consequently, the DPDNet does not require a great number of training samples for parameter optimization, and its computational efficiency is greatly enhanced. Extensive experiments have been conducted on five SAR datasets to verify the proposed DPDNet. The experimental results demonstrate that our method outperforms several state-of-the-art methods in change detection results.

Pixel and object-based land cover mapping and change detection from 1986 to 2020 for Hungary using histogram-based gradient boosting classification tree classifier

The large-scale pixel-based land use/land cover classification is a challenging task, which depends on many circumstances. This study aims to create LULC maps with the nomenclature of Coordination of Information on the Environment (CORINE) Land Cover (CLC) for years when the CLC databases are not available. Furthermore, testing the predicted maps for land use changes in the last 30 years in Hungary. Histogram-based gradient boosting classification tree (HGBCT) classifier was tested at classification. According to the results, the classifier, with the use of texture variance and landscape metrics is capable to generate accurate predicted maps, and the comparison of the predicted maps provides a detailed image of the land use changes.

Pixel mapping variable-resolution spectral imaging reconstruction

The predecessor of China Optics was China Optics and Applied Optics Digest, which was founded in 1985. At that time, it was the only retrieval journal in the field of optics in China. At the end of 2008, China Optics and Applied Optics Digest was renamed

PENGENALAN HURUF LATIN PADA ANAK USIA DINI DENGAN PENERAPAN METODE BACKPROPAGATION

Early childhood has difficulty remembering Latin letters or Roman characters than adults. Some of the factors that cause it are cognitive development, motivation, interest in learning, emotions and environmental factors. To overcome this, an innovative media is needed so that children can easily remember Latin letters. One of the innovative media applies digital image processing techniques and artificial intelligence. The fonts used are 10 types of letter models with image processing techniques such as preprocessing, binaryization, pixel mapping and creating vector as feature extraction. While the artificial intelligence used is the backpropagation method. The total data is 208 letter images with 625 input features with 500 epochs, the best learning rate used by the system is 0.025 so that the best training accuracy is 93.96% and testing accuracy is 92.31%.

Effective Brain Tumor Classification on MRI Using Deep Belief-convolutional Neural Network with Pixel Change Detection based on Pixel Mapping Technique

Brain tumor is a kind of cancer, in which tissues in the brain grows rapidly and unevenly in the brains and causes huge threats on human life. Brain tumor is recognized as one of the common dreadful cancers among adults and it also affects the children too. This kind of cancer is categorized into two types, such as benign tumor and malignant tumor. However, benign tumor is curable, whereas recovering of patients whoever affected by malignant tumor has less chance to survive. Nowadays, MR images are usually employed to detect the kinds of brain tumor. Early classification and identification of tumor is significant to treat the tumor and saves the human life from early death. However, the classification of brain tumor and percentage in change detection using pre-operative and post-operative MR images is a very challenging task. In order to overcome such issues, this research proposes a new effective technique for brain tumor classification and determination of pixel change detection using proposed Deep Belief Network (DBN) + Deep Convolutional Neural Network (DCNN). The process involves four phases, such as pre-processing, segmentation, feature extraction, and classification. The combination of DBN + CNN is employed for decision making based on error function. The DBN + CNN are trained utilizing the developed BirCat algorithm. Moreover, the proposed approach achieved a maximum accuracy of 0.957, sensitivity of 0.967, and specificity of 0.918.