Computational Analysis and Deep Learning for Medical Care

332 334

333 335

334 336

335 337

336 338

337 339

338 340

339 341

340 342

341 343

342 344

343 345

344 346

345 347

346 348

347 349

348 350

349 351

350 352

351 353

352 354

353 355

354 356

355 357

356 358

357 359

358 361

359 363

360 364

361 365

362 366

363 367

364 368

365 369

366 370

367 371

368 372

369 373

370 374

371 375

372 376

373 377

374 378

375 379

376 380

377 381

378 382

379 383

380 384

381 385

382 386

383 387

384 388

385 389

386 390

387 391

388 392

389 393

390 394

391 395

392 396

393 397

394 398

395 399

396 400

397 401

398 402

399 403

400 404

401 405

402 406

403 407

404 408

405 409

406 410

407 411

408 412

409 413

410 414

411 415

412 416

413 417

414 418

415 419

416 420

417 421

418 422

419 423

420 424

421 425

422 426

423 427

424 429

425 430

426 431

427 432

428 433

429 434

430 435

431 436

432 437

433 438

434 439

435 440

436 441

437 442

438 443

439 444

440 445

441 446

442 447

443 448

444 449

445 450

446 451

447 452

448 453

449 454

450 455

451 456

452 457

453 458

454 459

455 460

456 461

457 463

458 464

459 465

460 466

461 467

462 468

463 469

464 470

465 471

466 472

467 473

468 474

469 475

470 476

471 477

472 478

473 479

474 480

475 481

476 482

477 483

478 484

479 485

480 486

481 487

482 488

483 489

484 490

485 491

486 492

487 493

488 494

489 495

490 496

491 497

492 498

Scrivener Publishing100 Cummings Center, Suite 541J Beverly, MA 01915-6106

Publishers at Scrivener Martin Scrivener ( martin@scrivenerpublishing.com) Phillip Carmical ( pcarmical@scrivenerpublishing.com)

Edited by

Amit Kumar Tyagi

This edition first published 2021 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA

© 2021 Scrivener Publishing LLC

For more information about Scrivener publications please visit www.scrivenerpublishing.com.

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.

Wiley Global Headquarters111 River Street, Hoboken, NJ 07030, USA

For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com.

Limit of Liability/Disclaimer of WarrantyWhile the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials, or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read.

Library of Congress Cataloging-in-Publication Data

ISBN 9781119785729

Cover image: Pixabay.Com

Cover design by Russell Richardson

Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines

Printed in the USA

10 9 8 7 6 5 4 3 2 1

Due to recent technological developments and the integration of millions of Internet of Things (IoT)-connected devices, a large volume of data is being generated every day. This data, known as big data, is summed up by the 7 V’s—Volume, Velocity, Variety, Variability, Veracity, Visualization, and Value. Efficient tools, models and algorithms are required to analyze this data in order to advance the development of applications in several sectors, including e-healthcare (i.e., for disease prediction) and satellites (i.e., for weather prediction) among others. In the case of data related to biomedical imaging, this analyzed data is very useful to doctors and their patients in making predictive and effective decisions when treating disease. The healthcare sector needs to rely on smart machines/devices to collect data; however, nowadays, these smart machines/devices are facing several critical issues, including security breaches, data leaks of private information, loss of trust, etc.

We are currently entering the era of smart world devices, where robots or machines are being used in most applications to solve real-world problems. These smart machines/devices reduce the burden on doctors, which in turn make their lives easier and the lives of their patients better, thereby increasing patient longevity, which is the ultimate goal of computer vision. Therefore, our goal in writing this book is to attempt to provide complete information on reliable deep learning models required for e-healthcare applications. Ways in which deep learning can enhance healthcare images or text data for making useful decisions will be discussed. Also presented are reliable deep learning models, such as neural networks, convolutional neural networks, backpropagation, and recurrent neural networks, which are increasingly being used in medical image processing, including for colorization of black and white X-ray images, automatic machine translation images, object classification in photographs/images (CT scans), character or useful generation (ECG), image caption generation, etc. Hence, reliable deep learning methods for the perception or production of better results are a necessity for highly effective e-healthcare applications. Currently, the most difficult data-related problem that needs to be solved concerns the rapid increase of data occurring each day via billions of smart devices. To address the growing amount of data in healthcare applications, challenges such as not having standard tools, efficient algorithms, and a sufficient number of skilled data scientists need to be faced. Hence, there is growing interest in investigating deep learning models and their use in e-healthcare applications.